Balancing Constraints and Rewards with Meta-Gradient D4PG

Dan A. Calian · Daniel J Mankowitz · Tom Zahavy · Zhongwen Xu · Junhyuk Oh · Nir Levine · Timothy A Mann

Keywords: [ constraints ] [ meta-gradients ] [ reinforcement learning ]


Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluation procedure exists). This results in solutions where a task cannot be solved without violating the constraints. However, in many real-world cases, constraint violations are undesirable yet they are not catastrophic, motivating the need for soft-constrained RL approaches. We present two soft-constrained RL approaches that utilize meta-gradients to find a good trade-off between expected return and minimizing constraint violations. We demonstrate the effectiveness of these approaches by showing that they consistently outperform the baselines across four different Mujoco domains.

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