Proximal Policy Optimization (Reinforcement Learning)Thom Lane
The document discusses Proximal Policy Optimization (PPO), a policy gradient method for reinforcement learning. Some key points:
- PPO directly learns the policy rather than values like Q-functions. It can handle both discrete and continuous action spaces.
- Policy gradient methods estimate the gradient of expected return with respect to the policy parameters. Basic updates involve taking a step in the direction of this gradient.
- PPO improves stability over basic policy gradients by clipping the objective to constrain the policy update. It also uses multiple losses including for the value function and entropy.
- Actor-critic methods like PPO learn the policy (actor) and estimated state value (critic) simultaneously. The critic acts as
1. Policy gradient methods estimate the optimal policy through gradient ascent on the expected return. They directly learn stochastic policies without estimating value functions.
2. REINFORCE uses Monte Carlo returns to estimate the policy gradient. It updates the policy parameters in the direction of the gradient to maximize expected returns.
3. PPO improves upon REINFORCE by clipping the objective function to restrict how far the new policy can be from the old policy, which helps stabilize training. It uses a surrogate objective and importance sampling to train the policy on data collected from previous policies.
Lecture 18: Gaussian Mixture Models and Expectation Maximizationbutest
This document discusses Gaussian mixture models (GMMs) and the expectation-maximization (EM) algorithm. GMMs model data as coming from a mixture of Gaussian distributions, with each data point assigned soft responsibilities to the different components. EM is used to estimate the parameters of GMMs and other latent variable models. It iterates between an E-step, where responsibilities are computed based on current parameters, and an M-step, where new parameters are estimated to maximize the expected complete-data log-likelihood given the responsibilities. EM converges to a local optimum for fitting GMMs to data.
This document describes a project to create a web-based e-learning tool called the Pathfinding Visualizer to visualize shortest path algorithms like Dijkstra's algorithm. The tool allows users to select an algorithm, place nodes on a grid, and visualize each step of the algorithm to find the shortest path. This helps improve understanding of complex graph algorithms through interactive visualization. The tool is intended to make computer science education more effective by bridging the gap between theoretical algorithms and practical understanding.
Proximal Policy Optimization (Reinforcement Learning)Thom Lane
The document discusses Proximal Policy Optimization (PPO), a policy gradient method for reinforcement learning. Some key points:
- PPO directly learns the policy rather than values like Q-functions. It can handle both discrete and continuous action spaces.
- Policy gradient methods estimate the gradient of expected return with respect to the policy parameters. Basic updates involve taking a step in the direction of this gradient.
- PPO improves stability over basic policy gradients by clipping the objective to constrain the policy update. It also uses multiple losses including for the value function and entropy.
- Actor-critic methods like PPO learn the policy (actor) and estimated state value (critic) simultaneously. The critic acts as
1. Policy gradient methods estimate the optimal policy through gradient ascent on the expected return. They directly learn stochastic policies without estimating value functions.
2. REINFORCE uses Monte Carlo returns to estimate the policy gradient. It updates the policy parameters in the direction of the gradient to maximize expected returns.
3. PPO improves upon REINFORCE by clipping the objective function to restrict how far the new policy can be from the old policy, which helps stabilize training. It uses a surrogate objective and importance sampling to train the policy on data collected from previous policies.
Lecture 18: Gaussian Mixture Models and Expectation Maximizationbutest
This document discusses Gaussian mixture models (GMMs) and the expectation-maximization (EM) algorithm. GMMs model data as coming from a mixture of Gaussian distributions, with each data point assigned soft responsibilities to the different components. EM is used to estimate the parameters of GMMs and other latent variable models. It iterates between an E-step, where responsibilities are computed based on current parameters, and an M-step, where new parameters are estimated to maximize the expected complete-data log-likelihood given the responsibilities. EM converges to a local optimum for fitting GMMs to data.
This document describes a project to create a web-based e-learning tool called the Pathfinding Visualizer to visualize shortest path algorithms like Dijkstra's algorithm. The tool allows users to select an algorithm, place nodes on a grid, and visualize each step of the algorithm to find the shortest path. This helps improve understanding of complex graph algorithms through interactive visualization. The tool is intended to make computer science education more effective by bridging the gap between theoretical algorithms and practical understanding.
RLKorea의 프로젝트인 피지여행에서 진행한 내용을 정리한 것입니다. 피지여행은 DeepRL에서 중요한 Policy Gradient를 쭉 정리해보는 프로젝트입니다. PG의 처음 시작인 REINFORCE 부터 현재 새로운 baseline이 된 PPO까지 이론과 코드를 함께 살펴봅니다.
The document discusses general problem solving in artificial intelligence. It defines key concepts like problem space, state space, operators, initial and goal states. Problem solving involves searching the state space to find a path from the initial to the goal state. Different search algorithms can be used, like depth-first search and breadth-first search. Heuristic functions can guide searches to improve efficiency. Constraint satisfaction problems are another class of problems that can be solved using techniques like backtracking.
Regularization and variable selection via elastic netKyusonLim
The document summarizes the Elastic Net regularization method for variable selection in datasets with more predictors than observations (p > n). It describes how the Elastic Net overcomes limitations of LASSO and Ridge Regression by performing automatic variable selection, continuous shrinkage, and selecting groups of correlated predictors. The Naive Elastic Net formulation is presented, along with how it relates to LASSO and Ridge penalties. Computational details of the Elastic Net, including the LARS-EN algorithm and simulations, are discussed.
The document describes the implementation of a math typesetting module in the Satyrographos programming language. It defines types like math and math-element to represent mathematical expressions. Functions like output and output-element are used to convert a math expression to a string by mapping over its elements. Special characters are wrapped in calls to make-char-info to handle properties like color. Let-math is used to define relational operators like ≤ by combining character, class, and color information.
This document provides an overview of cluster analysis and clustering algorithms. It defines cluster analysis as grouping objects such that objects within a group are similar to each other and different from objects in other groups. The document discusses different types of clusters, including well-separated, prototype-based, contiguity-based, and density-based clusters. It also covers hierarchical and partitional clustering. Finally, it describes the widely used k-means clustering algorithm and its objective function.
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
The document discusses the Playfair cipher encryption method. It consists of generating a 5x5 key square using the keyword, with each letter appearing only once. To encrypt a pair of letters: if they are in the same column, take the letters below; if the same row, take the letters to the right; otherwise, take the letters at the opposite corners of the rectangle formed by the letters. An example is then provided to encrypt the plaintext "cryptography" using the keyword "playfair".
The document provides an overview of the A* pathfinding algorithm. It begins by defining the pathfinding problem and describing applications like game development. It then introduces the A* algorithm, which finds the shortest path between nodes in a graph by considering both the distance traveled so far (like in Dijkstra's algorithm) and the estimated distance to the goal (like in best-first search). The document provides an example of A* in action and discusses how heuristics can guide the search while ensuring an optimal solution. It also compares the running time of A* to Dijkstra's algorithm.
Local beam search begins with k randomly generated states and generates the successors of those states at each step. It selects the k best successors based on a heuristic and abandons the others, repeating this process to quickly focus on the most promising searches. A stochastic version chooses successors probabilistically based on their goodness. The search assumes a fixed beam width and performs breadth-first exploration while only keeping the best new nodes at each level according to the heuristic.
The document discusses Getac rugged computers, outlining their history of innovation in rugged hardware since 1989, their global presence, key technologies around materials, displays and certifications, and product lines including fully rugged tablets, notebooks and mobile servers designed for demanding work environments.
This document proposes using hyperbolic space to embed hierarchical tree structures, like those that can represent sequences of events in reinforcement learning problems. Specifically, it suggests a method called S-RYM that applies spectral normalization to regularize gradients when training deep reinforcement learning agents with hyperbolic embeddings. This stabilization technique allows naive hyperbolic embeddings to outperform standard Euclidean embeddings. It works by reducing gradient norm explosions during training, allowing the entropy loss to converge properly. The document provides technical details on spectral normalization, hyperbolic space representations, and how S-RYM trains deep reinforcement learning agents with stabilized hyperbolic embeddings.
This document proposes using linear function approximation as a computationally efficient method for solving reinforcement learning problems compared to neural network approaches like TRPO and PPO. It summarizes TRPO and PPO, which use neural networks to approximate value functions. The paper then presents a natural actor-critic algorithm that uses linear function approximation instead of neural networks for value approximation. The author evaluates this approach on cart pole and acrobot benchmarks and finds it trains faster than neural network methods while achieving equivalent or better results, especially on sparse reward problems. This allows the paper to recommend using natural policy gradient methods with linear function approximation over TRPO and PPO for traditional and sparse reward low-dimensional reinforcement learning challenges.
RLKorea의 프로젝트인 피지여행에서 진행한 내용을 정리한 것입니다. 피지여행은 DeepRL에서 중요한 Policy Gradient를 쭉 정리해보는 프로젝트입니다. PG의 처음 시작인 REINFORCE 부터 현재 새로운 baseline이 된 PPO까지 이론과 코드를 함께 살펴봅니다.
The document discusses general problem solving in artificial intelligence. It defines key concepts like problem space, state space, operators, initial and goal states. Problem solving involves searching the state space to find a path from the initial to the goal state. Different search algorithms can be used, like depth-first search and breadth-first search. Heuristic functions can guide searches to improve efficiency. Constraint satisfaction problems are another class of problems that can be solved using techniques like backtracking.
Regularization and variable selection via elastic netKyusonLim
The document summarizes the Elastic Net regularization method for variable selection in datasets with more predictors than observations (p > n). It describes how the Elastic Net overcomes limitations of LASSO and Ridge Regression by performing automatic variable selection, continuous shrinkage, and selecting groups of correlated predictors. The Naive Elastic Net formulation is presented, along with how it relates to LASSO and Ridge penalties. Computational details of the Elastic Net, including the LARS-EN algorithm and simulations, are discussed.
The document describes the implementation of a math typesetting module in the Satyrographos programming language. It defines types like math and math-element to represent mathematical expressions. Functions like output and output-element are used to convert a math expression to a string by mapping over its elements. Special characters are wrapped in calls to make-char-info to handle properties like color. Let-math is used to define relational operators like ≤ by combining character, class, and color information.
This document provides an overview of cluster analysis and clustering algorithms. It defines cluster analysis as grouping objects such that objects within a group are similar to each other and different from objects in other groups. The document discusses different types of clusters, including well-separated, prototype-based, contiguity-based, and density-based clusters. It also covers hierarchical and partitional clustering. Finally, it describes the widely used k-means clustering algorithm and its objective function.
Problem Characteristics in Artificial Intelligence,
Unit -2 Problem Solving and Searching Techniques
o choose an appropriate method for a particular problem first we need to categorize the problem based on the following characteristics.
Is the problem decomposable into small sub-problems which are easy to solve?
Can solution steps be ignored or undone?
Is the universe of the problem is predictable?
Is a good solution to the problem is absolute or relative?
Is the solution to the problem a state or a path?
What is the role of knowledge in solving a problem using artificial intelligence?
Does the task of solving a problem require human interaction?
1. Is the problem decomposable into small sub-problems which are easy to solve?
Can the problem be broken down into smaller problems to be solved independently?
See also Water Jug Problem in Artificial Intelligence
The decomposable problem can be solved easily.
Example: In this case, the problem is divided into smaller problems. The smaller problems are solved independently. Finally, the result is merged to get the final result.
Is the problem decomposable
2. Can solution steps be ignored or undone?
In the Theorem Proving problem, a lemma that has been proved can be ignored for the next steps.
Such problems are called Ignorable problems.
In the 8-Puzzle, Moves can be undone and backtracked.
Such problems are called Recoverable problems.
In Playing Chess, moves can be retracted.
Such problems are called Irrecoverable problems.
Ignorable problems can be solved using a simple control structure that never backtracks. Recoverable problems can be solved using backtracking. Irrecoverable problems can be solved by recoverable style methods via planning.
3. Is the universe of the problem is predictable?
In Playing Bridge, We cannot know exactly where all the cards are or what the other players will do on their turns.
Uncertain outcome!
For certain-outcome problems, planning can be used to generate a sequence of operators that is guaranteed to lead to a solution.
For uncertain-outcome problems, a sequence of generated operators can only have a good probability of leading to a solution. Plan revision is made as the plan is carried out and the necessary feedback is provided.
4. Is a good solution to the problem is absolute or relative?
The Travelling Salesman Problem, we have to try all paths to find the shortest one.
See also Generate and Test Heuristic Search - Artificial Intelligence
Any path problem can be solved using heuristics that suggest good paths to explore.
For best-path problems, a much more exhaustive search will be performed.
5. Is the solution to the problem a state or a path
The Water Jug Problem, the path that leads to the goal must be reported.
Lecture slides in DASI spring 2018, National Cheng Kung University, Taiwan. The content is about deep reinforcement learning: policy gradient including variance reduction and importance sampling
The document discusses the Playfair cipher encryption method. It consists of generating a 5x5 key square using the keyword, with each letter appearing only once. To encrypt a pair of letters: if they are in the same column, take the letters below; if the same row, take the letters to the right; otherwise, take the letters at the opposite corners of the rectangle formed by the letters. An example is then provided to encrypt the plaintext "cryptography" using the keyword "playfair".
The document provides an overview of the A* pathfinding algorithm. It begins by defining the pathfinding problem and describing applications like game development. It then introduces the A* algorithm, which finds the shortest path between nodes in a graph by considering both the distance traveled so far (like in Dijkstra's algorithm) and the estimated distance to the goal (like in best-first search). The document provides an example of A* in action and discusses how heuristics can guide the search while ensuring an optimal solution. It also compares the running time of A* to Dijkstra's algorithm.
Local beam search begins with k randomly generated states and generates the successors of those states at each step. It selects the k best successors based on a heuristic and abandons the others, repeating this process to quickly focus on the most promising searches. A stochastic version chooses successors probabilistically based on their goodness. The search assumes a fixed beam width and performs breadth-first exploration while only keeping the best new nodes at each level according to the heuristic.
The document discusses Getac rugged computers, outlining their history of innovation in rugged hardware since 1989, their global presence, key technologies around materials, displays and certifications, and product lines including fully rugged tablets, notebooks and mobile servers designed for demanding work environments.
This document proposes using hyperbolic space to embed hierarchical tree structures, like those that can represent sequences of events in reinforcement learning problems. Specifically, it suggests a method called S-RYM that applies spectral normalization to regularize gradients when training deep reinforcement learning agents with hyperbolic embeddings. This stabilization technique allows naive hyperbolic embeddings to outperform standard Euclidean embeddings. It works by reducing gradient norm explosions during training, allowing the entropy loss to converge properly. The document provides technical details on spectral normalization, hyperbolic space representations, and how S-RYM trains deep reinforcement learning agents with stabilized hyperbolic embeddings.
This document proposes using linear function approximation as a computationally efficient method for solving reinforcement learning problems compared to neural network approaches like TRPO and PPO. It summarizes TRPO and PPO, which use neural networks to approximate value functions. The paper then presents a natural actor-critic algorithm that uses linear function approximation instead of neural networks for value approximation. The author evaluates this approach on cart pole and acrobot benchmarks and finds it trains faster than neural network methods while achieving equivalent or better results, especially on sparse reward problems. This allows the paper to recommend using natural policy gradient methods with linear function approximation over TRPO and PPO for traditional and sparse reward low-dimensional reinforcement learning challenges.
SEARN is an algorithm for structured prediction that casts it as a sequence of cost-sensitive classification problems. It works by learning a policy to make incremental decisions that build up the full structured output. The policy is trained through an iterative process of generating cost-sensitive examples from sample outputs produced by the current policy, training a classifier on those examples, and interpolating the new policy with the previous one. This allows SEARN to learn the structured prediction task without requiring assumptions about the output structure, unlike approaches that make independence assumptions or rely on global prediction models.
A presentation about NGBoost (Natural Gradient Boosting) which I presented in the Information Theory and Probabilistic Programming course at the University of Oklahoma.
Artificial Intelligence Course: Linear models ananth
In this presentation we present the linear models: Regression and Classification. We illustrate with several examples. Concepts such as underfitting (Bias) and overfitting (Variance) are presented. Linear models can be used as stand alone classifiers for simple cases and they are essential building blocks as a part of larger deep learning networks
AUTOMATIC TRANSFER RATE ADJUSTMENT FOR TRANSFER REINFORCEMENT LEARNINGgerogepatton
This paper proposes a novel parameter for transfer reinforcement learning to avoid over-fitting when an
agent uses a transferred policy from a source task. Learning robot systems have recently been studied for
many applications, such as home robots, communication robots, and warehouse robots. However, if the
agent reuses the knowledge that has been sufficiently learned in the source task, deadlock may occur and
appropriate transfer learning may not be realized. In the previous work, a parameter called transfer rate
was proposed to adjust the ratio of transfer, and its contribution include avoiding dead lock in the target
task. However, adjusting the parameter depends on human intuition and experiences. Furthermore, the
method for deciding transfer rate has not discussed. Therefore, an automatic method for adjusting the
transfer rate is proposed in this paper using a sigmoid function. Further, computer simulations are used to
evaluate the effectiveness of the proposed method to improve the environmental adaptation performance in
a target task, which refers to the situation of reusing knowledge.
Continuous Control with Deep Reinforcement Learning, lillicrap et al, 2015Chris Ohk
The paper introduces Deep Deterministic Policy Gradient (DDPG), a model-free reinforcement learning algorithm for problems with continuous action spaces. DDPG combines actor-critic methods with experience replay and target networks similar to DQN. It uses a replay buffer to minimize correlations between samples and target networks to provide stable learning targets. The algorithm was able to solve challenging control problems with high-dimensional observation and action spaces, demonstrating the ability of deep reinforcement learning to handle complex, continuous control tasks.
1. The document describes the Behavior Regularized Actor Critic (BRAC) framework, which evaluates different design choices for offline reinforcement learning algorithms.
2. BRAC experiments show that simple variants using a fixed regularization weight, minimum ensemble Q-targets, and value penalty regularization can achieve good performance, outperforming more complex techniques from previous work.
3. The experiments find that choices like the divergence used for regularization and number of ensemble Q-functions do not have large impacts on performance, and hyperparameter sensitivity also varies between design choices.
Reinforcement Learning Guide For Beginnersgokulprasath06
Reinforcement Learning Guide:
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1118_Seminar_Continuous_Deep Q-Learning with Model based accelerationHye-min Ahn
The document summarizes a research paper titled "Continuous Deep Q-Learning with Model-based Acceleration" presented at ICML 2016. It proposes a method that incorporates advantages of both model-free and model-based reinforcement learning. The method uses deep Q-learning with normalized advantage functions to learn a parameterized Q-function for continuous state-action spaces. It accelerates the learning process by using trajectory optimization from an imagined model to generate exploratory behaviors during data collection.
How to formulate reinforcement learning in illustrative waysYasutoTamura1
This lecture introduces reinforcement learning and how to approach learning it. It discusses formulating the environment as a Markov decision process and defines important concepts like policy, value functions, returns, and the Bellman equation. The key ideas are that reinforcement learning involves optimizing a policy to maximize expected returns, and value functions are introduced to indirectly evaluate and improve the policy through dynamic programming methods like policy iteration and value iteration. Understanding these fundamental concepts through simple examples is emphasized as the starting point for learning reinforcement learning.
Evolving Reinforcement Learning Algorithms, JD. Co-Reyes et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Evolving Reinforcement Learning Algorithms 논문 내용을 정리해 발표했습니다. 이 논문은 Value-based Model-free RL 에이전트의 손실 함수를 표현하는 언어를 설계하고 기존 DQN보다 최적화된 손실 함수를 제안합니다. 많은 분들에게 도움이 되었으면 합니다.
Adapted Branch-and-Bound Algorithm Using SVM With Model SelectionIJECEIAES
Branch-and-Bound algorithm is the basis for the majority of solving methods in mixed integer linear programming. It has been proving its efficiency in different fields. In fact, it creates little by little a tree of nodes by adopting two strategies. These strategies are variable selection strategy and node selection strategy. In our previous work, we experienced a methodology of learning branch-and-bound strategies using regression-based support vector machine twice. That methodology allowed firstly to exploit information from previous executions of Branch-and-Bound algorithm on other instances. Secondly, it created information channel between node selection strategy and variable branching strategy. And thirdly, it gave good results in term of running time comparing to standard Branch-and-Bound algorithm. In this work, we will focus on increasing SVM performance by using cross validation coupled with model selection.
This document provides an overview of deep reinforcement learning and related concepts. It discusses reinforcement learning techniques such as model-based and model-free approaches. Deep reinforcement learning techniques like deep Q-networks, policy gradients, and actor-critic methods are explained. The document also introduces decision transformers, which transform reinforcement learning into a sequence modeling problem, and multi-game decision transformers which can learn to play multiple games simultaneously.
This document summarizes the NGBoost method for probabilistic regression. NGBoost uses gradient boosting to fit the parameters of an assumed probabilistic distribution for the target variable. It improves on existing probabilistic regression methods by using the natural gradient, which performs gradient descent in the space of distributions rather than the parameter space. This addresses issues with prior approaches and allows NGBoost to achieve state-of-the-art performance while remaining fast, flexible, and scalable. Future work may apply NGBoost to other problems like survival analysis or joint outcome regression.
This document discusses Bayesian optimization for hyperparameter tuning. It begins by introducing the modelling workflow of a data scientist and focusing on the model selection step. It then explains that most models have hyperparameters that need to be chosen and the goal is to find hyperparameters that optimize model performance on holdout data. Bayesian optimization is introduced as a method for hyperparameter tuning that builds a probabilistic model of performance using Gaussian processes. It iteratively evaluates hyperparameters chosen by an acquisition function that balances exploitation and exploration. An example of using Bayesian optimization to tune an SVM's hyperparameters is provided, along with practical considerations and open-source tools.
Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic of how the program will work before developing the actual code for a program.
COMPARISON BETWEEN THE GENETIC ALGORITHMS OPTIMIZATION AND PARTICLE SWARM OPT...IAEME Publication
Close range photogrammetry network design is referred to the process of placing a set of
cameras in order to achieve photogrammetric tasks. The main objective of this paper is tried to find
the best location of two/three camera stations. The genetic algorithm optimization and Particle
Swarm Optimization are developed to determine the optimal camera stations for computing the three
dimensional coordinates. In this research, a mathematical model representing the genetic algorithm
optimization and Particle Swarm Optimization for the close range photogrammetry network is
developed. This paper gives also the sequence of the field operations and computational steps for this
task. A test field is included to reinforce the theoretical aspects.
Comparison between the genetic algorithms optimization and particle swarm opt...IAEME Publication
The document compares the genetic algorithms optimization and particle swarm optimization methods for designing close range photogrammetry networks. It presents the genetic algorithm and particle swarm optimization as two popular meta-heuristic algorithms inspired by natural evolution and collective animal behavior, respectively. The document develops mathematical models representing the genetic algorithm and particle swarm optimization for close range photogrammetry network design and evaluates them in a test field to reinforce the theoretical aspects.
Similar to Proximal Policy Optimization Algorithms, Schulman et al, 2017 (20)
Momenti Seminar - 5 Years of RosettaStoneChris Ohk
Momenti Seminar에서 진행했던 "하스스톤 시뮬레이터 RosettaStone 개발 5년 간의 기록"의 발표 자료를 공유드립니다. 5년 동안 오픈 소스 프로젝트를 진행하면서 경험했던 일들을 정리하며 어떤 교훈을 얻었는지 생각해보는 시간이었습니다. 많은 분들에게 도움이 되었으면 합니다.
Adversarially Guided Actor-Critic, Y. Flet-Berliac et al, 2021Chris Ohk
RL 논문 리뷰 스터디에서 Adversarially Guided Actor-Critic 논문 내용을 정리해 발표했습니다. AGAC는 Actor-Critic에 GAN에서 영감을 받은 방법들을 결합해 리워드가 희소하고 탐험이 어려운 환경에서 뛰어난 성능을 보여줍니다. 많은 분들에게 도움이 되었으면 합니다.
Agent57: Outperforming the Atari Human Benchmark, Badia, A. P. et al, 2020Chris Ohk
RL 논문 리뷰 스터디에서 Agent57 논문 내용을 정리해 발표했습니다. Agent57은 NGU(Never Give Up)를 기반으로 몇 가지 기능을 개선해 57개의 Atari 게임 모두 인간보다 뛰어난 점수를 기록한 최초의 RL 알고리즘입니다. 많은 분들에게 도움이 되었으면 합니다.
GDG Gwangju DevFest 2019 - <하스스톤> 강화학습 환경 개발기Chris Ohk
강화학습에 관심을 갖게 되어 어떤 게임에 적용해볼까 고민하다가 평소 즐기던 '하스스톤'이라는 게임에 관심을 갖게 되어 2017년 말부터 하스스톤 강화 학습을 위한 API를 만들기 시작했습니다. 이 발표를 통해 평소 하스스톤과 같은 카드 게임 개발이나 게임에 강화학습을 적용하기 위한 환경을 구축하는데 관심을 갖고 있던 프로그래머들에게 조금이나마 도움이 되었으면 합니다.
모던 C++의 시초인 C++11은 C++ 코드 전반에 많은 변화를 가져왔습니다. 그리고 최근 C++20의 표준위원회 회의가 마무리되었습니다. 내년에 C++20이 도입되면 C++11이 처음 도입되었을 때와 비슷한 규모, 또는 그 이상의 변화가 있을 것이라고 예상하고 있습니다. C++20에는 Concepts, Contract, Ranges, Coroutine, Module 등 굵직한 기능 외에도 많은 기능들이 추가될 예정입니다. 이번 세션에서는 C++20에 추가될 주요 기능들을 살펴보고자 합니다.
GDG Campus Korea에서 개최한 'Daily 만년 Junior들의 이야기 : 델리만주' 밋업에서 발표했던 내용으로 대학원 석사 입학 후부터 오늘날까지 어떤 활동들을 했는지 정리했습니다. 대학원생 분들과 게임 프로그래머 취업을 준비하시는 분들께 많은 도움이 되었으면 합니다.
오픈소스 개발을 시작하기로 결정했더라도, 처음 개발하는 경우에는 막상 무엇을 개발할지, 그리고 어떻게 개발해야 할 지 막막하기만 합니다. 이 때는 기존에 공개되어 있는 오픈소스 프로젝트를 활용해 개선해나가는 프로젝트부터 시작하면 많은 도움이 됩니다. 이번 강연에서는 기존 오픈소스 프로젝트를 처음부터 새로 만들어가면서 개선해나갔던 경험을 이야기하고 어떻게 하면 오픈소스 개발에 쉽게 접근할 수 있는지를 알려줍니다.
C++은 10년 만에 C++11/14를 발표하면서 '모던 C++'이라는 이름으로 발전했습니다. 그만큼 새로운 기능들이 많이 추가되었습니다. 그리고 2017년, C++은 C++17이라는 이름으로 또 한 번의 발전을 준비하고 있습니다. 3년 주기로 빠르게 변화하는 모던 C++에 대비하기 위해, C++17에 추가될 주요 기능들을 살펴보고자 합니다.
이 발표는 이전에 발표했던 내용에서 일부 사례 추가 및 최신 내용으로 갱신한 버전입니다.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
2. Proximal Policy Optimization Algorithms, Schulman et al, 2017Introduction
• In recent years, several different approaches have been
proposed for reinforcement learning with neural network
function approximators. The leading contenders are deep Q-
learning, “vanilla” policy gradient methods, and trust region /
natural policy gradient methods.
• However, there is room for improvement in developing a
method that is scalable (to large models and parallel
implementations), data efficient, and robust (i.e., successful
on a variety of problems without hyperparameter tuning).
3. Proximal Policy Optimization Algorithms, Schulman et al, 2017Introduction
• DQN
• fails on many simple problems and is poorly understood
• A3C – “Vanilla” policy gradient methods
• have poor data efficiency and robustness
• TRPO
• relatively complicated
• is not compatible with architectures that include noise (such as dropout) or
parameter sharing (between the policy and value function, or with auxiliary
tasks)
4. Proximal Policy Optimization Algorithms, Schulman et al, 2017Introduction
• We propose a novel objective with clipped probability ratios,
which forms a pessimistic estimate (i.e., lower bound) of the
performance of the policy.
• This paper seeks to improve the current state of affairs by
introducing an algorithm that attains the data efficiency and
reliable performance of TRPO, while using only first-order
optimization.
5. Proximal Policy Optimization Algorithms, Schulman et al, 2017Introduction
• To optimize policies, we alternate between sampling data from
the policy and performing several epochs of optimization on
the sampled data.
6. Proximal Policy Optimization Algorithms, Schulman et al, 2017Introduction
• Our experiments compare the performance of various different
versions of the surrogate objective, and find that the version
with the clipped probability ratios performs best.
• We also compare PPO to several previous algorithms from the
literature.
• On continuous control tasks, it performs better than the algorithms we
compare against.
• On Atari, it performs significantly better (in terms of sample complexity) than
A2C and similarly to ACER though it is much simpler.
7. PPO, Schulman et al, 2017Background: Policy Optimization
• Policy gradient methods work by computing an estimator of
the policy gradient and plugging it into a stochastic gradient
ascent algorithm. The most commonly used gradient estimator
has the form
ො𝑔 = 𝔼 𝑡 ∇ 𝜃 log 𝜋 𝜃 𝑎 𝑡 𝑠𝑡
መ𝐴 𝑡
• where 𝜋 𝜃 is a stochastic policy and መ𝐴 𝑡 is an estimator of the advantage
function at timestep 𝑡. Here, the expectation 𝔼 𝑡 … indicates the empirical
average over a finite batch of samples, in an algorithm that alternates
between sampling and optimization.
8. PPO, Schulman et al, 2017Background: Policy Optimization
• Implementations that use automatic differentiation software
work by constructing an objective function whose gradient is
the policy gradient estimator; the estimator ො𝑔 is obtained by
differentiating the objective
𝐿 𝑃𝐺
𝜃 = 𝔼 𝑡 log 𝜋 𝜃 𝑎 𝑡 𝑠𝑡
መ𝐴 𝑡
9. PPO, Schulman et al, 2017Background: Policy Optimization
• While it is appealing to perform multiple steps of optimization
on this loss 𝐿 𝑃𝐺
using the same trajectory, doing so is not well-
justified, and empirically it often leads to destructively large
policy updates.
10. PPO, Schulman et al, 2017Background: Policy Optimization
• In TRPO, an objective function (the “surrogate” objective) is
maximized subject to a constraint on the size of the policy
update. Specifically,
maximize
𝜃
𝔼 𝑡
𝜋 𝜃(𝑎 𝑡|𝑠𝑡)
𝜋 𝜃old
(𝑎 𝑡|𝑠𝑡)
መ𝐴 𝑡
subject to 𝔼 𝑡 𝐾𝐿 𝜋 𝜃old
∙ 𝑠𝑡 , 𝜋 𝜃 ∙ 𝑠𝑡 ≤ 𝛿
• Here, 𝜃old is the vector of policy parameters before the update.
11. PPO, Schulman et al, 2017Background: Policy Optimization
• This problem can efficiently be approximately solved using the
conjugate gradient algorithm, after making a linear
approximation to the objective and a quadratic approximation
to the constraint.
12. PPO, Schulman et al, 2017Background: Policy Optimization
• The theory justifying TRPO actually suggests using a penalty
instead of a constraint, i.e., solving the unconstrained
optimization problem for some coefficient 𝛽.
maximize
𝜃
𝔼 𝑡
𝜋 𝜃(𝑎 𝑡|𝑠𝑡)
𝜋 𝜃old
(𝑎 𝑡|𝑠𝑡)
መ𝐴 𝑡 − 𝛽𝐾𝐿 𝜋 𝜃old
∙ 𝑠𝑡 , 𝜋 𝜃 ∙ 𝑠𝑡
13. PPO, Schulman et al, 2017Background: Policy Optimization
• This follows from the fact that a certain surrogate objective
(which computes the max KL over states instead of the mean)
forms a lower bound (i.e., a pessimistic bound) on the
performance of the policy 𝜋.
• TRPO uses a hard constraint rather than a penalty because it is
hard to choose a single value of 𝜷 that performs well across
different problems—or even within a single problem, where the
characteristics change over the course of learning.
14. PPO, Schulman et al, 2017Clipped Surrogate Objective
• Let 𝑟𝑡(𝜃) denote the probability ratio 𝑟𝑡 𝜃 =
𝜋 𝜃(𝑎 𝑡|𝑠 𝑡)
𝜋 𝜃old
(𝑎 𝑡|𝑠 𝑡)
,
so 𝑟𝑡 𝜃old = 1. TRPO maximizes a “surrogate” objective
𝐿 𝐶𝑃𝐼
𝜃 = 𝔼 𝑡
𝜋 𝜃(𝑎 𝑡|𝑠𝑡)
𝜋 𝜃old
(𝑎 𝑡|𝑠𝑡)
መ𝐴 𝑡 = 𝔼 𝑡 𝑟𝑡 𝜃 መ𝐴 𝑡
• The superscript CPI refers to conservative policy iteration,
where this objective was proposed.
15. PPO, Schulman et al, 2017Clipped Surrogate Objective
• Without a constraint, maximization of 𝐿 𝐶𝑃𝐼
would lead to an
excessively large policy update; hence, we now consider how
to modify the objective, to penalize changes to the policy that
move 𝑟𝑡(𝜃) away from 1.
• The main objective we propose is the following:
𝐿 𝐶𝐿𝐼𝑃
𝜃 = 𝔼 𝑡 min(𝑟𝑡 𝜃 መ𝐴 𝑡, clip(𝑟𝑡 𝜃 , 1 − 𝜖, 1 + 𝜖) መ𝐴 𝑡)
• where epsilon is a hyperparameter, say, 𝜖 = 0.2.
16. PPO, Schulman et al, 2017Clipped Surrogate Objective
• The motivation for this objective is as follows.
• The first term inside the min is 𝐿 𝐶𝑃𝐼 𝜃 .
• The second term, clip(𝑟𝑡 𝜃 , 1 − 𝜖, 1 + 𝜖) መ𝐴 𝑡, modifies the surrogate objective
by clipping the probability ratio, which removes the incentive for moving
𝑟𝑡 𝜃 outside of the interval 1 − 𝜖, 1 + 𝜖 .
• Finally, we take the minimum of the clipped and unclipped objective, so the
final objective is a lower bound (i.e., a pessimistic bound) on the unclipped
objective.
17. PPO, Schulman et al, 2017Clipped Surrogate Objective
Figure 1: Plots showing one term (i.e., a single timestep) of the surrogate function
𝐿 𝐶𝐿𝐼𝑃
as a function of the probability ratio 𝑟, for positive advantages (left) and negative
advantages (right). The red circle on each plot shows the starting point for the
optimization, i.e., 𝑟 = 1. Note that 𝐿 𝐶𝐿𝐼𝑃
sums many of these terms.
18. PPO, Schulman et al, 2017Clipped Surrogate Objective
Figure 2: Surrogate objectives, as we interpolate between the initial policy parameter
𝜃old, and the updated policy parameter, which we compute after one iteration of PPO.
The updated policy has a KL divergence of about 0.02 from the initial policy, and this is
the point at which 𝐿 𝐶𝐿𝐼𝑃
is maximal.
19. PPO, Schulman et al, 2017Adaptive KL Penalty Coefficient
• Another approach, which can be used as an alternative to the
clipped surrogate objective, or in addition to it, is to use a
penalty on KL divergence, and to adapt the penalty coefficient
so that we achieve some target value of the KL divergence
𝑑targ each policy update.
• In our experiments, we found that the KL penalty performed worse than the
clipped surrogate objective, however, we’ve included it here because it’s an
important baseline.
20. PPO, Schulman et al, 2017Adaptive KL Penalty Coefficient
• In the simplest instantiation of this algorithm, we perform the
following steps in each policy update:
• Using several epochs of minibatch SGD, optimize the KL-penalized objective
𝐿 𝐾𝐿𝑃𝐸𝑁 𝜃 = 𝔼 𝑡
𝜋 𝜃(𝑎 𝑡|𝑠𝑡)
𝜋 𝜃old
(𝑎 𝑡|𝑠𝑡)
መ𝐴 𝑡 − 𝛽𝐾𝐿 𝜋 𝜃old
∙ 𝑠𝑡 , 𝜋 𝜃 ∙ 𝑠𝑡
• Compute 𝒅 = 𝔼 𝒕 𝑲𝑳 𝝅 𝜽 𝒐𝒍𝒅
∙ 𝒔 𝒕 , 𝝅 𝜽 ∙ 𝒔 𝒕
• If 𝑑 < Τ𝑑 𝑡𝑎𝑟𝑔 1.5 , 𝛽 ← Τ𝛽 2
• If 𝑑 > 𝑑 𝑡𝑎𝑟𝑔 × 1.5, 𝛽 ← 𝛽 × 2
• The updated 𝛽 is used for the next policy update.
21. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• Most techniques for computing variance-reduced advantage-
function estimators make use a learned state-value function 𝑉(𝑠)
• Generalized advantage estimation
• The finite-horizon estimators
22. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• If using a neural network architecture that shares parameters
between the policy and value function, we must use a loss function
that combines the policy surrogate and a value function error term.
• This objective can further be augmented by adding an entropy bonus
to ensure sufficient exploration, as suggested in past work (A3C).
23. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• Combining these terms, we obtain the following objective,
which is (approximately) maximized each iteration:
𝐿 𝑡
𝐶𝐿𝐼𝑃+𝑉𝐹+𝑆
𝜃 = 𝔼 𝑡 𝐿 𝑡
𝐶𝐿𝐼𝑃
𝜃 − 𝑐1 𝐿 𝑡
𝑉𝐹
𝜃 + 𝑐2 𝑆 𝜋 𝜃 𝑠𝑡
• where 𝑐1, 𝑐2 are coefficients, and 𝑆 denotes an entropy bonus,
and 𝐿 𝑡
𝑉𝐹
is a squared-error loss 𝑉𝜃 𝑠𝑡 − 𝑉𝑡
targ 2
.
24. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• One style of policy gradient implementation, popularized in
A3C and well-suited for use with recurrent neural networks,
runs the policy for 𝑻 timesteps (where 𝑻 is much less than the
episode length), and uses the collected samples for an update.
25. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• This style requires an advantage estimator that does not look
beyond timestep 𝑻. The estimator used by A3C is
መ𝐴 𝑡 = −𝑉 𝑠𝑡 + 𝑟𝑡 + 𝛾𝑟𝑡+1 + ⋯ + 𝛾 𝑇−𝑡+1
𝑟 𝑇−1 + 𝛾 𝑇−𝑡
𝑉 𝑠 𝑇
• where 𝑡 specifies the time index in 0, 𝑇 ,
within a given length-𝑇 trajectory segment.
26. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• Generalizing this choice, we can use a truncated version of
generalized advantage estimation, which reduces to previous
equation when 𝜆 = 1:
መ𝐴 𝑡 = 𝛿𝑡 + 𝛾𝜆 𝛿𝑡+1 + ⋯ + ⋯ + 𝛾𝜆 𝑇−𝑡+1
𝛿 𝑇−1
where 𝛿𝑡 = 𝑟𝑡 + 𝛾𝑉 𝑠𝑡+1 − 𝑉 𝑠𝑡
27. Proximal Policy Optimization Algorithms, Schulman et al, 2017Algorithm
• A proximal policy optimization (PPO) algorithm that uses
fixed-length trajectory segments is shown below.
• Each iteration, each of 𝑁 (parallel) actors collect 𝑇 timesteps of data.
• Then we construct the surrogate loss on these 𝑁𝑇 timesteps of data, and
optimize it with minibatch SGD (or usually for better performance, Adam),
for 𝐾 epochs.
28. Proximal Policy Optimization Algorithms, Schulman et al, 2017Experiments
• First, we compare several different surrogate objectives under
different hyperparameters. Here, we compare the surrogate
objective 𝐿 𝐶𝐿𝐼𝑃
to several natural variations and ablated
versions.
• No clipping or penalty: 𝐿 𝑡(𝜃) = 𝑟𝑡 𝜃 መ𝐴 𝑡
• Clipping: 𝐿 𝑡(𝜃) = min(𝑟𝑡 𝜃 መ𝐴 𝑡, clip(𝑟𝑡 𝜃 , 1 − 𝜖, 1 + 𝜖) መ𝐴 𝑡)
• KL penalty (fixed or adaptive): 𝐿 𝑡 𝜃 = 𝑟𝑡 𝜃 መ𝐴 𝑡 − 𝛽𝐾𝐿 𝜋 𝜃old
, 𝜋 𝜃
33. Proximal Policy Optimization Algorithms, Schulman et al, 2017Conclusion
• We have introduced proximal policy optimization, a family of
policy optimization methods that use multiple epochs of
stochastic gradient ascent to perform each policy update.
34. Proximal Policy Optimization Algorithms, Schulman et al, 2017Conclusion
• These methods have the stability and reliability of trust-region
methods but are much simpler to implement, requiring only
few lines of code change to a vanilla policy gradient (A3C)
implementation, applicable in more general settings (for
example, when using a joint architecture for the policy and
value function), and have better overall performance.