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Learning safe solutions is an important but challenging problem in multi-agent reinforcement learning (MARL). Shielded reinforcement learning is one approach for preventing agents from choosing unsafe actions. Current shielded reinforcement learning methods for MARL make strong assumptions about communication and full observability. In this work, we extend the formalization of the shielded reinforcement learning problem to a decentralized multi-agent setting. We then present an algorithm for decomposition of a centralized shield, allowing shields to be used in such decentralized, communication-free environments. Our results show that agents equipped with decentralized shields perform comparably to agents with centralized shields in several tasks, allowing shielding to be used in environments with decentralized training and execution for the first time.
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Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably. Ideally, agents should learn and execute asynchronously instead. Such asynchronous methods also allow temporally extended actions that can take different amounts of time based on the situation and action executed. Unfortunately, current policy gradient methods are not applicable in asynchronous settings, as they assume that agents synchronously reason about action selection at every time step. To allow asynchronous learning and decision-making, we formulate a set of asynchronous multi-agent actor-critic methods that allow agents to directly optimize asynchronous policies in three standard training paradigms: decentralized learning, centralized learning, and centralized training for decentralized execution. Empirical results (in simulation and hardware) in a variety of realistic domains demonstrate the superiority of our approaches in large multi-agent problems and validate the effectiveness of our algorithms for learning high-quality and asynchronous solutions.
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- Award ID(s):
- 2044993
- PAR ID:
- 10389824
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Multi-Agent Reinforcement Learning can be used to learn solutions for a wide variety of tasks, but there are few safety guarantees about the policies that the agents learn. My research addresses the challenge of ensuring safety in communication-free multi-agent environments, using shielding as the primary tool. We introduce methods to completely prevent safety violations in domains for which a model is available, in both fully observable and partially observable environments. We present ongoing research on maximizing safety in environments for which no model is available, utilizing a centralized training, decentralized execution framework, and discuss future lines of research.more » « less
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
