Reinforcement Learning 10. On-policy Control with Approximation
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A summary of Chapter 10: On-policy Control with Approximation of the book 'Reinforcement Learning: An Introduction' by Sutton and Barto. You can find the full book in Professor Sutton's website: http://incompleteideas.net/book/the-book-2nd.html
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Lecture slides of DSAI 2018 in National Cheng Kung University.
Reinforcement Learning: Temporal-difference Learning, including Sarsa, Q-learning, n-step bootstrapping, eligibility trace.
Dexterous In-hand Manipulation by OpenAI
OpenAI has used Reinforcement Learning to train a humanoid robotic hand to rotate a cube to achieve any desired orientation. This is discussed in arXiv:1808.00177, 2019 and in the blog <openai.com/blog/learning dexterity/>. These slides present results from the paper along with a few important concepts in reinforcement learning I learnt through many other sources.
Quality management
A 20 minutes presentation on Quality Management which covered generic things. Includes 3 steps of Quality Management and rule of thumb (80/20).
Preprocessing presentation
A short presentation on preprocessing data and feature scaling. It highlights the rationale behind scaling your continuous variables and the different methods for doing so.
Uber Data Analysis - SAS Project
This document summarizes an analysis of Uber pickup data from New York City. Several machine learning models were tested, including decision trees, regression, and neural networks. The best model was a decision tree, which found that the top factors affecting Uber pickups were borough, time of day, day of week, and temperature. Pickups were highest in Manhattan, after business hours, on weekends, and when temperatures were lower. To reduce surge pricing, the supply and demand of drivers needs to be balanced during periods of high demand.
DQN (Deep Q-Network)
The document discusses Deep Q-Network (DQN), which combines Q-learning with deep neural networks to allow for function approximation and solving problems with large state/action spaces. DQN uses experience replay and a separate target network to stabilize training. It has led to many successful variants, including Double DQN which reduces overestimation, prioritized experience replay which replays important transitions more frequently, and dueling networks which separate value and advantage estimation.
Augmenting Decisions of Taxi Drivers through Reinforcement Learning for Impro...
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Structured prediction with reinforcement learning
This document introduces the framework of structured prediction Markov decision processes (SP-MDP) which links structured prediction and reinforcement learning. It discusses how SP problems can be formulated as SP-MDPs by defining states, actions, transitions, and rewards. Approximated reinforcement learning algorithms like Q-learning, SARSA, and policy gradients can then be used to find optimal policies for structured prediction in the SP-MDP framework. This allows reinforcement learning techniques to be applied to structured prediction problems.
Making Complex Decisions(Artificial Intelligence)
This chapter shows how to use knowledge about the wlorld to make decisions even when the
outcomes of an action are uncertain and the rewards for acting might not be reaped until many
actions have passed. The main points are as follows:
e Sequential decision problems in uncertain envirsinments,also called Markov decision
processes, or MDPs, are defined by a transition model specifying the probabilistic
outcomes of actions and a reward function specifying the reward in each state.
o The utility of a state sequence is the sum of all the rewards over the sequence, possibly
discounted over time. The solution of an MDP is a policy that associates a decision
with every state that the agent might reach. An optimal policy maximizes the utility of
the state sequences encountered when it is execut~ed.
e The utility of a state is the expected utility of the state sequences encountered when
an optimal policy is executed, starting in that state. The value iteration algorithm for
solving MDPs works by iteratively solving the equations relating the utilities of each
state to that of its neighbors.
Policy iteration alternates between calculating the utilities of states under the current
policy and improving the current policy with respect to the current utilities.
* Partially observable MDPs, or POMDPs, are much more difficult to solve than are
MDPs. They can be solved by conversion to an MDP in the continuous space of belief
states. Optimal behavior in POMDPs includes information gathering to reduce uncertainty and therefore make better decisions in the fiuture.
A decision-theoretic agent can be constructed for POMDP environments. The agent
uses a dynamic decision network to represent the transition and observation models,
to update its belief state, and to project forward possible action sequences.
Game theory describes rational behavior for agents in situations where multiple agents
interact simultaneously. Solutions of games are Nash equilibria-strategy profiles in
which no agent has an incentive to deviate from the specified strategy.
Mechanism design can be used to set the rules by which agents will interact, in order
to maximize some global utility through the operation of individually rational agents.
Sometimes, mechanisms exist that achieve this goal without requiring each agent to
consider the choices made by other agents.
We shall return to the world of MDPs and POMDP in Chapter 21, when we study reinforcement learning methods that allow an agent to improve its behavior from experience in sequential, uncertain environments.
Deep Q-learning from Demonstrations DQfD
Deep Q-learning from Demonstrations (DQfD) is a method that leverages expert demonstrations to accelerate deep Q-learning. It pre-trains the Q-network solely on demonstration data using supervised learning and reinforcement learning losses. During interaction with the environment, it continues to update the network using demonstration data in the replay buffer. DQfD outperformed the worst and best demonstrations in many games and achieved state-of-the-art performance in some games, demonstrating that it can leverage demonstrations to solve problems more efficiently than alternative methods.
Mastering Async/Await in JavaScript
This document outlines the schedule and content for a workshop on mastering async/await in Node.js. The workshop covers using async/await to handle asynchronous code, error handling, and composing async functions. It includes two exercises - one to gather blog post comments from an API, and another to retry failed requests. The key takeaways are that async functions always return promises, await pauses execution until a promise settles, and returned values/errors are handled via promise resolution/rejection.
Introduction: Asynchronous Methods for Deep Reinforcement Learning
The document introduces asynchronous reinforcement learning methods. It discusses standard reinforcement learning concepts like Markov decision processes, value functions, and Q-learning. It then presents the asynchronous advantage actor-critic (A3C) algorithm, which uses multiple asynchronous agents with shared parameters to improve stability. Experiments show A3C outperforms DQN on Atari games and car racing tasks, training faster without specialized hardware. A3C also scales well to multiple CPU cores and is robust to learning rate and initialization.
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Reinforcement Learning 10. On-policy Control with Approximation
- 1. Chapter 10: On-policy Control with Approximation Seungjae Ryan Lee
- 2. Episodic 1-step semi-gradient Sarsa ● Approximate action values (instead of state values) ● Use Sarsa to define target ● Converges the same ways as TD(0) with same error bound
- 3. Control with Episodic 1-step semi-gradient Sarsa ● Select action and improve policy using an ε-greedy action w.r.t.
- 4. Mountain Car Example ● Task: Drive an underpowered car up a steep mountain road ○ Gravity is stronger than car’s engine ○ Must swing back and forth to build enough inertia ● State: position , velocity ● Actions: Forward (+1), Reverse (-1), No-op (0) ● Reward: -1 until the goal is reached
- 5. Approximation for Mountain Car ● Tile coding used to select binary features (8 tiles)
- 6. Results of Mountain Car ● Plot the cost-to-go function: ● Initial action values set to 0 ○ Very optimistic
- 7. Results of Mountain Car
- 8. Episodic n-step Semi-gradient Sarsa ● Use n-step return as the update target
- 9. Episodic n-step Semi-gradient Sarsa in Practice
- 10. Episodic n-step Semi-gradient Sarsa Results ● Faster learning ● Better asymptotic performance
- 11. Episodic n-step Semi-gradient Sarsa Results ● Best performance for intermediate values of n-step
- 12. Average Reward Setting ● Quality of policy defined by the average reward following policy ● Continuing tasks without discounting
- 13. Differential Return and Value Functions Differential Return: differences between rewards and average reward Differential Value Functions: Expected differential returns
- 14. Bellman Equations ● Remove all ● Replace rewards with difference of rewards
- 15. Differential semi-gradient Sarsa ● Same update rule with differential TD error ● Original TD error: ● Differential TD error:
- 17. Access-Control Queuing Example ● Agent can grant access to 10 servers ○ Agent can accept or reject customers ● Customers arrive at a single queue ○ Customers have 4 different priorities, randomly distributed ○ Pay a reward of 1, 2, 4, or 8 when granted access to a server ● A busy server is freed with some probability
- 18. Access-Control Queuing Results ● Tabular solution with differential semi-gradient Sarsa
- 19. n-step Semi-gradient Sarsa ● Use n-step return ○
- 20. Thank you! Original content from ● Reinforcement Learning: An Introduction by Sutton and Barto You can find more content in ● github.com/seungjaeryanlee ● www.endtoend.ai