  Safe exploration is crucial for the real-world application of reinforcement
learning (RL). Previous works consider the safe exploration problem as
Constrained Markov Decision Process (CMDP), where the policies are being
optimized 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 RL
problems under the framework of Early Terminated MDP (ET-MDP). We first define
the ET-MDP as an unconstrained MDP with the same optimal value function as its
corresponding CMDP. An off-policy algorithm based on context models is then
proposed to solve the ET-MDP, which thereby solves the corresponding CMDP with
better asymptotic performance and improved learning efficiency. Experiments on
various CMDP tasks show a substantial improvement over previous methods that
directly solve CMDP.
