Counterfactual Multi-Agent Policy Gradients

Cooperative multi-agent systems can be naturally used to model many real world problems, such as network packet routing and the coordination of autonomous vehicles. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actor-critic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.

[1]  Mahesan Niranjan,et al.  On-line Q-learning using connectionist systems , 1994 .

[2]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[3]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[4]  Shigenobu Kobayashi,et al.  An Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function , 1998, ICML.

[5]  John N. Tsitsiklis,et al.  Actor-Critic Algorithms , 1999, NIPS.

[6]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[7]  Lex Weaver,et al.  The Optimal Reward Baseline for Gradient-Based Reinforcement Learning , 2001, UAI.

[8]  Kagan Tumer,et al.  Optimal Payoff Functions for Members of Collectives , 2001, Adv. Complex Syst..

[9]  Leslie Pack Kaelbling,et al.  All learning is Local: Multi-agent Learning in Global Reward Games , 2003, NIPS.

[10]  Erfu Yang,et al.  Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey , 2004 .

[11]  Ronald J. Williams Simple statistical gradient-following algorithms for connectionist reinforcement learning , 2004, Machine Learning.

[12]  Wang Ying,et al.  Multi-agent framework for third party logistics in E-commerce , 2005, Expert Syst. Appl..

[13]  Danny Weyns,et al.  The Packet-World: A Test Bed for Investigating Situated Multi-Agent Systems , 2005 .

[14]  Richard S. Sutton,et al.  Learning to Predict by the Methods of Temporal Differences , 1988, Machine Learning.

[15]  Kagan Tumer,et al.  Distributed agent-based air traffic flow management , 2007, AAMAS '07.

[16]  Nikos A. Vlassis,et al.  Optimal and Approximate Q-value Functions for Decentralized POMDPs , 2008, J. Artif. Intell. Res..

[17]  Bart De Schutter,et al.  A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[18]  Yoav Shoham,et al.  Multiagent Systems - Algorithmic, Game-Theoretic, and Logical Foundations , 2008 .

[19]  Clément Farabet,et al.  Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.

[20]  Martin A. Riedmiller,et al.  Reinforcement learning in feedback control , 2011, Machine Learning.

[21]  Kagan Tumer,et al.  Modeling difference rewards for multiagent learning , 2012, AAMAS.

[22]  Wenwu Yu,et al.  An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination , 2012, IEEE Transactions on Industrial Informatics.

[23]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[24]  Kevin Leyton-Brown,et al.  Empirically Evaluating Multiagent Learning Algorithms , 2014, ArXiv.

[25]  Yoshua Bengio,et al.  Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.

[26]  Kagan Tumer,et al.  Approximating Difference Evaluations with Local Information , 2015, AAMAS.

[27]  Yun Yang,et al.  A Multi-Agent Framework for Packet Routing in Wireless Sensor Networks , 2015, Sensors.

[28]  Peter Stone,et al.  Deep Recurrent Q-Learning for Partially Observable MDPs , 2015, AAAI Fall Symposia.

[29]  Shimon Whiteson,et al.  Learning to Communicate with Deep Multi-Agent Reinforcement Learning , 2016, NIPS.

[30]  Bikramjit Banerjee,et al.  Multi-agent reinforcement learning as a rehearsal for decentralized planning , 2016, Neurocomputing.

[31]  Rob Fergus,et al.  Learning Multiagent Communication with Backpropagation , 2016, NIPS.

[32]  Sergey Levine,et al.  High-Dimensional Continuous Control Using Generalized Advantage Estimation , 2015, ICLR.

[33]  Florian Richoux,et al.  TorchCraft: a Library for Machine Learning Research on Real-Time Strategy Games , 2016, ArXiv.

[34]  Nicolas Usunier,et al.  Episodic Exploration for Deep Deterministic Policies: An Application to StarCraft Micromanagement Tasks , 2016, ArXiv.

[35]  Emil Gustavsson,et al.  Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence , 2016, ArXiv.

[36]  Shimon Whiteson,et al.  Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning , 2017, ICML.

[37]  Mykel J. Kochenderfer,et al.  Cooperative Multi-agent Control Using Deep Reinforcement Learning , 2017, AAMAS Workshops.

[38]  Nando de Freitas,et al.  Sample Efficient Actor-Critic with Experience Replay , 2016, ICLR.

[39]  Peng Peng,et al.  Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games , 2017, 1703.10069.

[40]  Yi Wu,et al.  Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.

[41]  Dorian Kodelja,et al.  Multiagent cooperation and competition with deep reinforcement learning , 2015, PloS one.

[42]  Jun Wang,et al.  Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games , 2017, ArXiv.

[43]  Jonathan P. How,et al.  Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability , 2017, ICML.

[44]  Jonathan P. How,et al.  Deep Decentralized Multi-task Multi-Agent RL under Partial Observability , 2017 .

[45]  Stefan Lee,et al.  Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[46]  Joel Z. Leibo,et al.  Multi-agent Reinforcement Learning in Sequential Social Dilemmas , 2017, AAMAS.

[47]  Alexander Peysakhovich,et al.  Multi-Agent Cooperation and the Emergence of (Natural) Language , 2016, ICLR.

[48]  Pieter Abbeel,et al.  Emergence of Grounded Compositional Language in Multi-Agent Populations , 2017, AAAI.