Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

Abstract

In recent years we have seen fast progress on a number of benchmark problems in AI, with modern methods achieving near or super human performance in Go, Poker and Dota. One common aspect of all of these challenges is that they are by design adversarial or, technically speaking, zero-sum. In contrast to these settings, success in the real world commonly requires humans to collaborate and communicate with others, in settings that are, at least partially, cooperative. In the last year, the card game Hanabi has been established as a new benchmark environment for AI to fill this gap. In particular, Hanabi is interesting to humans since it is entirely focused on theory of mind, i.e., the ability to effectively reason over the intentions, beliefs and point of view of other agents when observing their actions. Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies. However, when done naively, this randomness will inherently make their actions less informative to others during training. We present a new deep multi-agent RL method, the Simplified Action Decoder (SAD), which resolves this contradiction exploiting the centralized training phase. During training SAD allows other agents to not only observe the (exploratory) action chosen, but agents instead also observe the greedy action of their team mates. By combining this simple intuition with best practices for multi-agent learning, SAD establishes a new SOTA for learning methods for 2-5 players on the self-play part of the Hanabi challenge. Our ablations show the contributions of SAD compared with the best practice components. All of our code and trained agents are available at https://github.com/facebookresearch/Hanabi_SAD.

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Cited by in corpus (9)

• "Other-Play" for Zero-Shot Coordination
• Learning to Play No-Press Diplomacy with Best Response Policy Iteration
• Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi
• Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level
• Continuous Coordination As a Realistic Scenario for Lifelong Learning
• Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi
• Off-Belief Learning
• Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates
• Augmenting the action space with conventions to improve multi-agent cooperation in Hanabi