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Constrained Reinforcement Learning has been employed to enforce safetyconstraints on policy through the use of expected cost constraints. The keychallenge is in handling expected cost accumulated using the policy and notjust in a single step. Existing methods have developed innovative ways ofconverting this cost constraint over entire policy to constraints over localdecisions (at each time step). While such approaches have provided goodsolutions with regards to objective, they can either be overly aggressive orconservative with respect to costs. This is owing to use of estimates for"future" or "backward" costs in local cost constraints.  To that end, we provide an equivalent unconstrained formulation toconstrained RL that has an augmented state space and reward penalties. Thisintuitive formulation is general and has interesting theoretical properties.More importantly, this provides a new paradigm for solving constrained RLproblems effectively. As we show in our experimental results, we are able tooutperform leading approaches on multiple benchmark problems from literature.

Solving Richly Constrained Reinforcement Learning through State Augmentation and Reward Penalties