Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces

• URL: http://arxiv.org/abs/2108.03952v2
• Date: Wed, 11 Aug 2021 09:25:11 GMT
• Title: Safe Deep Reinforcement Learning for Multi-Agent Systems with Continuous Action Spaces
• Abstract summary: We enhance the well-known multi-agent deep deterministic policy gradient (MADDPG) framework by adding a safety layer to the deep policy network. We propose to circumvent infeasibility problems in the action correction step using soft constraints.
• Score: 5.553946791700077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-agent control problems constitute an interesting area of application for deep reinforcement learning models with continuous action spaces. Such real-world applications, however, typically come with critical safety constraints that must not be violated. In order to ensure safety, we enhance the well-known multi-agent deep deterministic policy gradient (MADDPG) framework by adding a safety layer to the deep policy network. In particular, we extend the idea of linearizing the single-step transition dynamics, as was done for single-agent systems in Safe DDPG (Dalal et al., 2018), to multi-agent settings. We additionally propose to circumvent infeasibility problems in the action correction step using soft constraints (Kerrigan & Maciejowski, 2000). Results from the theory of exact penalty functions can be used to guarantee constraint satisfaction of the soft constraints under mild assumptions. We empirically find that the soft formulation achieves a dramatic decrease in constraint violations, making safety available even during the learning procedure.

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