Playing the Snake Game with Deep Reinforcement Learning (by Chuyang Liu)
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This document summarizes work on using deep reinforcement learning to play the Snake game. It discusses: 1) Background on deep reinforcement learning algorithms like DQN that were applied to games like Atari 2600. 2) The author's implementation of a DQN using a global/local state vector to represent the Snake environment. 3) Experiments comparing the performance of standard DQN to optimizations like Double DQN, Prioritized Experience Replay, and Dueling Network structures.
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Playing the Snake Game with Deep Reinforcement Learning (by Chuyang Liu)
- 1. Playing the Snake Game with Deep Reinforcement Learning Chuyang Liu https://github.com/chuyangliu/snake
- 3. Background 2015 2016 2017 DQN Optimizations DDQN, Prioritized, Duel Human-Level Control through Deep Reinforcement Learning (DeepMind) Deep Q-Network (DQN) Snake AI (Hawstein, Tobin Ehlis, Christopher Lockhart, …) Graph Theory, GP, RL, … Snake AI (Chuyang Liu) Path Search, Hamilton Cycle Benchmark: Atari 2600 BFS, Longest Path
- 4. My Works ? Designed global/local state vector Discussed absolute/relative move direction Feature Extraction Implemented Deep Q-Network (Experience Replay & Fixed Target-Net) DQN Compared the performance of the three optimizations for DQN DQN Optimizations Designed a better reward policy improving performance Environment
- 5. 𝜽 ← 𝜽 + 𝜼 𝝏 𝝏𝜽 𝒓 + 𝜸𝐦𝐚𝐱 𝒂′ 𝑸 𝒕𝒂𝒓𝒈𝒆𝒕 𝒔′ , 𝒂′ ; 𝜽′ − 𝑸 𝒆𝒗𝒂𝒍(𝒔, 𝒂; 𝜽) 𝟐 Policy 𝑎 ← 𝜋 𝑠 Eval-NetTarget-Net Q-Target Q-Eval Loss state action state’reward Environment 𝑟, 𝑠′ ← 𝑒𝑛𝑣 𝑠, 𝑎 Algorithm: Computation Graph assign
- 6. Environment 𝑟, 𝑠′ ← 𝑒𝑛𝑣 𝑠, 𝑎 Algorithm: Environment FOOD +1.0 DEAD -0.5 (game over) ELSE -0.005 𝜽 ← 𝜽 + 𝜼 𝝏 𝝏𝜽 𝒓 + 𝜸𝐦𝐚𝐱 𝒂′ 𝑸 𝒕𝒂𝒓𝒈𝒆𝒕 𝒔′ , 𝒂′ ; 𝜽′ − 𝑸 𝒆𝒗𝒂𝒍(𝒔, 𝒂; 𝜽) 𝟐 Policy 𝑎 ← 𝜋 𝑠 state action state’reward
- 7. 𝜽 ← 𝜽 + 𝜼 𝝏 𝝏𝜽 𝒓 + 𝜸𝐦𝐚𝐱 𝒂′ 𝑸 𝒕𝒂𝒓𝒈𝒆𝒕 𝒔′ , 𝒂′ ; 𝜽′ − 𝑸 𝒆𝒗𝒂𝒍(𝒔, 𝒂; 𝜽) 𝟐 Policy 𝑎 ← 𝜋 𝑠 Eval-NetTarget-Net Q-Target Q-Eval Loss state action state’reward Environment 𝑟, 𝑠′ ← 𝑒𝑛𝑣 𝑠, 𝑎 Algorithm: Computation Graph assign
- 8. Eval-NetTarget-Net Algorithm: Network & State Vector assign
- 9. Target-Net Algorithm: Network & State Vector global (8x8x4) local (1x3) Conv3x3x32 Conv3x3x64 Conv2x2x128 Conv2x2x256 FC1024 FC512 Advantage1x3 Value1x1 Q1x3 FC512 Eval-Net Same structure but different weights and bias assign
- 10. Experiments
- 11. Experiments
- 12. Summary Double DQN Prioritized Experience Replay Dueling Network Structure DQN Feature Extraction Environment Feedback State & Action Human experience Pixels input Network Inputs Deep Neural Network Deep Q-Learning from Demonstrations (2018) Training Efficiency