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Deploying Reinforcement Learning (RL) agents to solve real-world applicationsoften requires satisfying complex system constraints. Often the constraintthresholds are incorrectly set due to the complex nature of a system or theinability to verify the thresholds offline (e.g, no simulator or reasonableoffline evaluation procedure exists). This results in solutions where a taskcannot be solved without violating the constraints. However, in many real-worldcases, constraint violations are undesirable yet they are not catastrophic,motivating the need for soft-constrained RL approaches. We present asoft-constrained RL approach that utilizes meta-gradients to find a goodtrade-off between expected return and minimizing constraint violations. Wedemonstrate the effectiveness of this approach by showing that it consistentlyoutperforms the baselines across four different MuJoCo domains.

Balancing Constraints and Rewards with Meta-Gradient D4PG