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Safe exploration is crucial for the real-world application of reinforcementlearning (RL). Previous works consider the safe exploration problem asConstrained Markov Decision Process (CMDP), where the policies are beingoptimized under constraints. However, when encountering any potential dangers,human tends to stop immediately and rarely learns to behave safely in danger.Motivated by human learning, we introduce a new approach to address safe RLproblems under the framework of Early Terminated MDP (ET-MDP). We first definethe ET-MDP as an unconstrained MDP with the same optimal value function as itscorresponding CMDP. An off-policy algorithm based on context models is thenproposed to solve the ET-MDP, which thereby solves the corresponding CMDP withbetter asymptotic performance and improved learning efficiency. Experiments onvarious CMDP tasks show a substantial improvement over previous methods thatdirectly solve CMDP.

Safe Exploration by Solving Early Terminated MDP