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


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.

Chat is not available.