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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. II Learning to Communicate
  2. 7. Bayesian Action Decoder
  3. 7.3 Method

7.3.5 Self-Consistent Beliefs

Previous7.3.4 Factorized Belief UpdatesNext7.4 Experiments and Results

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์‚ฌ์‹ค ์ด์ „์— ๊ทผ์‚ฌํ–ˆ๋˜ factorization์„ ๋‹ค์‹œ ์ƒ๊ฐํ•ด๋ณด๋ฉด, ๋‚ด๊ฐ€ ๋“ค๊ณ  ์žˆ๋Š” ์–ด๋–ค ์นด๋“œ๋“ค์— ์˜ํ•ด ์ƒ๋Œ€๋ฐฉ์ด ๋“ค๊ณ  ์žˆ์„ ์นด๋“œ๋Š” ์˜ํ–ฅ์„ ๋ฐ›์„ ์ˆ˜ ๋ฐ–์— ์—†์Šต๋‹ˆ๋‹ค. ํ•˜์ง€๋งŒ ์ด๋ฅผ ๋ฌด์‹œํ•œ ์ฑ„ ์–ด์ฉ” ์ˆ˜ ์—†๋Š” ๊ทผ์‚ฌ๋ฅผ ํ•˜์˜€์Šต๋‹ˆ๋‹ค.

๊ฒŒ๋‹ค๊ฐ€ ์ด๋Ÿฐ ๊ทผ์‚ฌ๋ฅผ ํ†ตํ•ด์„  ๊ฒฐ๊ณผ์ ์œผ๋กœ marginalํ•˜์ง€ ์•Š์€ fprif^{\mathrm{pri}}fpri๋ฅผ ๊ตฌํ•˜๊ฒŒ ๋  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ทธ ๊ฒƒ์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์ด ํ›„๋กœ๋Š” BAD์˜ ๋ฉ”์ธ ์•„์ด๋””์–ด๋Š” ์•„๋‹ˆ์ง€๋งŒ, ์–ด๋–ป๊ฒŒ marginalํ•˜๊ฒŒ Bt \mathcal{B}_tBtโ€‹๋ฅผ ์—…๋ฐ์ดํŠธํ•  ์ˆ˜ ์žˆ์„์ง€์— ๋Œ€ํ•œ iterative procedure๋ฅผ ์ œ์•ˆํ•ฉ๋‹ˆ๋‹ค.

B0=Bt \mathcal{B}^0 = \mathcal{B}_t B0=Btโ€‹

Bk+1(f[i])=โˆ‘f[โˆ’i]Bk(f[โˆ’i])P(f[i]โˆฃf[โˆ’i],fโ‰คtpub,uโ‰คta,ฯ€^โ‰คt)\mathcal{B}^{k+1}(f[i]) = \sum_{f[-i]}\mathcal{B}^k(f[-i])P(f[i]|f[-i],f^{\mathrm{pub}}_{\leq t}, u^a_{\leq t}, \hat{\pi}_{\leq t})Bk+1(f[i])=โˆ‘f[โˆ’i]โ€‹Bk(f[โˆ’i])P(f[i]โˆฃf[โˆ’i],fโ‰คtpubโ€‹,uโ‰คtaโ€‹,ฯ€^โ‰คtโ€‹)

โˆEf[โˆ’i]โˆผBk[Lt(f[i])P(f[i]โˆฃf[โˆ’i],ftpub)] \propto \mathbb{E}_{f[-i] \sim\mathcal{B}^k}[\mathcal{L}_t(f[i])P(f[i]|f[-i],f^{\mathrm{pub}}_t)]โˆEf[โˆ’i]โˆผBkโ€‹[Ltโ€‹(f[i])P(f[i]โˆฃf[โˆ’i],ftpubโ€‹)]

๋งˆ์ง€๋ง‰ ํ…€์€ expectation ํ˜•ํƒœ๋กœ ๋ณ€ํ™˜ํ•จ์œผ๋กœ์จ sampling์„ ๊ฐ€๋Šฅํ•˜๋„๋ก ํ•˜์˜€์Šต๋‹ˆ๋‹ค.