Density Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2106.12764v1
• Date: Thu, 24 Jun 2021 04:22:03 GMT
• Title: Density Constrained Reinforcement Learning
• Authors: Zengyi Qin, Yuxiao Chen, Chuchu Fan
• Abstract summary: We study constrained reinforcement learning from a novel perspective by setting constraints directly on state density functions. We leverage the duality between density functions and Q functions to develop an effective algorithm to solve the density constrained RL problem optimally. We prove that the proposed algorithm converges to a near-optimal solution with a bounded error even when the policy update is imperfect.
• Score: 9.23225507471139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study constrained reinforcement learning (CRL) from a novel perspective by setting constraints directly on state density functions, rather than the value functions considered by previous works. State density has a clear physical and mathematical interpretation, and is able to express a wide variety of constraints such as resource limits and safety requirements. Density constraints can also avoid the time-consuming process of designing and tuning cost functions required by value function-based constraints to encode system specifications. We leverage the duality between density functions and Q functions to develop an effective algorithm to solve the density constrained RL problem optimally and the constrains are guaranteed to be satisfied. We prove that the proposed algorithm converges to a near-optimal solution with a bounded error even when the policy update is imperfect. We use a set of comprehensive experiments to demonstrate the advantages of our approach over state-of-the-art CRL methods, with a wide range of density constrained tasks as well as standard CRL benchmarks such as Safety-Gym.

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