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Safety is a critical hurdle that limits the application of deep reinforcementlearning (RL) to real-world control tasks. To this end, constrainedreinforcement learning leverages cost functions to improve safety inconstrained Markov decision processes. However, such constrained RL methodsfail to achieve zero violation even when the cost limit is zero. This paperanalyzes the reason for such failure, which suggests that a proper costfunction plays an important role in constrained RL. Inspired by the analysis,we propose AutoCost, a simple yet effective framework that automaticallysearches for cost functions that help constrained RL to achieve zero-violationperformance. We validate the proposed method and the searched cost function onthe safe RL benchmark Safety Gym. We compare the performance of augmentedagents that use our cost function to provide additive intrinsic costs withbaseline agents that use the same policy learners but with only extrinsiccosts. Results show that the converged policies with intrinsic costs in allenvironments achieve zero constraint violation and comparable performance withbaselines.

AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning