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Deep Multi-Agent Reinforcement Learning
  • Deep Multi-Agent Reinforcement Learning
  • Abstract & Contents
    • Abstract
  • 1. Introduction
    • 1. INTRODUCTION
      • 1.1 The Industrial Revolution, Cognition, and Computers
      • 1.2 Deep Multi-Agent Reinforcement-Learning
      • 1.3 Overall Structure
  • 2. Background
    • 2. BACKGROUND
      • 2.1 Reinforcement Learning
      • 2.2 Multi-Agent Settings
      • 2.3 Centralized vs Decentralized Control
      • 2.4 Cooperative, Zero-sum, and General-Sum
      • 2.5 Partial Observability
      • 2.6 Centralized Training, Decentralized Execution
      • 2.7 Value Functions
      • 2.8 Nash Equilibria
      • 2.9 Deep Learning for MARL
      • 2.10 Q-Learning and DQN
      • 2.11 Reinforce and Actor-Critic
  • I Learning to Collaborate
    • 3. Counterfactual Multi-Agent Policy Gradients
      • 3.1 Introduction
      • 3.2 Related Work
      • 3.3 Multi-Agent StarCraft Micromanagement
      • 3.4 Methods
        • 3.4.1 Independent Actor-Critic
        • 3.4.2 Counterfactual Multi-Agent Policy Gradients
        • 3.4.2.1 baseline lemma
        • 3.4.2.2 COMA Algorithm
      • 3.5 Results
      • 3.6 Conclusions & Future Work
    • 4 Multi-Agent Common Knowledge Reinforcement Learning
      • 4.1 Introduction
      • 4.2 Related Work
      • 4.3 Dec-POMDP and Features
      • 4.4 Common Knowledge
      • 4.5 Multi-Agent Common Knowledge Reinforcement Learning
      • 4.6 Pairwise MACKRL
      • 4.7 Experiments and Results
      • 4.8 Conclusion & Future Work
    • 5 Stabilizing Experience Replay
      • 5.1 Introduction
      • 5.2 Related Work
      • 5.3 Methods
        • 5.3.1 Multi-Agent Importance Sampling
        • 5.3.2 Multi-Agent Fingerprints
      • 5.4 Experiments
        • 5.4.1 Architecture
      • 5.5 Results
        • 5.5.1 Importance Sampling
        • 5.5.2 Fingerprints
        • 5.5.3 Informative Trajectories
      • 5.6 Conclusion & Future Work
  • II Learning to Communicate
    • 6. Learning to Communicate with Deep Multi-Agent ReinforcementLearning
      • 6.1 Introduction
      • 6.2 Related Work
      • 6.3 Setting
      • 6.4 Methods
        • 6.4.1 Reinforced Inter-Agent Learning
        • 6.4.2 Differentiable Inter-Agent Learning
      • 6.5 DIAL Details
      • 6.6 Experiments
        • 6.6.1 Model Architecture
        • 6.6.2 Switch Riddle
        • 6.6.3 MNIST Games
        • 6.6.4 Effect of Channel Noise
      • 6.7 Conclusion & Future Work
    • 7. Bayesian Action Decoder
      • 7.1 Introduction
      • 7.2 Setting
      • 7.3 Method
        • 7.3.1 Public belief
        • 7.3.2 Public Belief MDP
        • 7.3.3 Sampling Deterministic Partial Policies
        • 7.3.4 Factorized Belief Updates
        • 7.3.5 Self-Consistent Beliefs
      • 7.4 Experiments and Results
        • 7.4.1 Matrix Game
        • 7.4.2 Hanabi
        • 7.4.3 Observations and Actions
        • 7.4.4 Beliefs in Hanabi
        • 7.4.5 Architecture Details for Baselines and Method
        • 7.4.6 Hyperparamters
        • 7.4.7 Results on Hanabi
      • 7.5 Related Work
        • 7.5.1 Learning to Communicate
        • 7.5.2 Research on Hanabi
        • 7.5.3 Belief State Methods
      • 7.6 Conclusion & Future Work
  • III Learning to Reciprocate
    • 8. Learning with Opponent-Learning Awareness
      • 8.1 Introduction
      • 8.2 Related Work
      • 8.3 Methods
        • 8.3.1 Naive Learner
        • 8.3.2 Learning with Opponent Learning Awareness
        • 8.3.3. Learning via Policy gradient
        • 8.3.4 LOLA with Opponent modeling
        • 8.3.5 Higher-Order LOLA
      • 8.4 Experimental Setup
        • 8.4.1 Iterated Games
        • 8.4.2 Coin Game
        • 8.4.3 Training Details
      • 8.5 Results
        • 8.5.1 Iterated Games
        • 8.5.2 Coin Game
        • 8.5.3 Exploitability of LOLA
      • 8.6 Conclusion & Future Work
    • 9. DiCE: The Infinitely Differentiable Monte Carlo Estimator
      • 9.1 Introduction
      • 9.2 Background
        • 9.2.1 Stochastic Computation Graphs
        • 9.2.2 Surrogate Losses
      • 9.3 Higher Order Gradients
        • 9.3.1 Higher Order Gradient Estimators
        • 9.3.2 Higher Order Surrogate Losses
        • 9.3.3. Simple Failing Example
      • 9.4 Correct Gradient Estimators with DiCE
        • 9.4.1 Implement of DiCE
        • 9.4.2 Casuality
        • 9.4.3 First Order Variance Reduction
        • 9.4.4 Hessian-Vector Product
      • 9.5 Case Studies
        • 9.5.1 Empirical Verification
        • 9.5.2 DiCE For multi-agent RL
      • 9.6 Related Work
      • 9.7 Conclusion & Future Work
  • Reference
    • Reference
  • After
    • ๋ณด์ถฉ
    • ์—ญ์ž ํ›„๊ธฐ
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  1. III Learning to Reciprocate
  2. 8. Learning with Opponent-Learning Awareness
  3. 8.5 Results

8.5.3 Exploitability of LOLA

Previous8.5.2 Coin GameNext8.6 Conclusion & Future Work

Last updated 4 years ago

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์ด๋ฒˆ section์—์„œ๋Š” LOLA๋ฅผ 1์ฐจ๊ทผ์‚ฌ ๋ง๊ณ  ์ข€๋” ๋†’์€ ์ฐจ์ˆ˜๋กœ ๊ทผ์‚ฌํ•˜๋Š” ๊ฒƒ์— ๋Œ€ํ•ด ์•Œ์•„๋ด…๋‹ˆ๋‹ค. IPD์—์„œ ์ •ํ™•ํ•œ expected discounted return์„ ๊ณ„์‚ฐํ•  ์ˆ˜ ์žˆ๋Š” ์ƒํ™ฉ์„ ๊ฐ€์ •ํ•  ๊ฒƒ์ธ๋ฐ, ์ด๋Š” Policy-gradient๋ฐฉ์‹์„ ์‚ฌ์šฉํ•  ๋•Œ, ์ฐจ์ˆ˜๊ฐ€ ์ปค์งˆ์ˆ˜๋ก variance๊ฐ€ ์ปค์ง€๊ธฐ ๋•Œ๋ฌธ์— ์ด๋ฅผ ํ†ต์ œํ•˜๊ธฐ ์œ„ํ•จ์ž…๋‹ˆ๋‹ค. ์ด๋ฅผ ๋‹ค์Œ ํ‘œ์™€ ๊ฐ™์€ 6๊ฐœ์˜ ๊ทธ๋ฃน์œผ๋กœ ๋‚˜๋ˆ„์–ด ๋น„๊ต๋ฅผ ํ•ด๋ณด์•˜์Šต๋‹ˆ๋‹ค.

LOLA-Ex agent๋Š” LOLA-Ex์™€ ๋งŒ๋‚˜์•ผ ๊ฐ€์žฅ ๋†’์€ return์„ ๋ฐ›๊ฒŒ๋˜์—ˆ๊ณ , naive agent๋“ค์„ LOLA-Ex agent์™€ ๊ฐ™์ด ํ–‰๋™ํ•˜๋„๋ก ๋งŒ๋“ค์—ˆ์Šต๋‹ˆ๋‹ค. ๊ทธ๋ฆฌ๊ณ  2nd-order LOLA๋Š” LOLA-Ex์™€ ํ•จ๊ป˜ ๊ทธ๋ฃน์ด ๋˜์—ˆ์„ ๋•Œ, ์ข‹์€ ํšจ๊ณผ๊ฐ€ ๋‚˜๊ธฐ๋ณด๋‹ค ์˜คํžˆ๋ ค LOLA-Ex๋ผ๋ฆฌ์˜ ๊ทธ๋ฃน๋ณด๋‹ค ๋‚ฎ์€ ์„ฑ๋Šฅ์„ ๋ณด์˜€์Šต๋‹ˆ๋‹ค. ์ด ์‹คํ—˜์ด LOLA๋ผ๋ฆฌ์˜ ๊ทธ๋ฃน์ด ์ ˆ๋Œ€์ ์ด๋ผ๋Š” ๊ฒƒ์€ ์•„๋‹ˆ์ง€๋งŒ gradient-based learning์„ ํ•  ๋•Œ๋„ ์–ด๋–ค ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ผ์ง€ ๋Œ€๋žต์ ์œผ๋กœ ์•Œ ์ˆ˜ ์žˆ์—ˆ์Šต๋‹ˆ๋‹ค.