Robust Reinforcement Learning in Motion Planning

While exploring to find better solutions, an agent performing on(cid:173) line reinforcement learning (RL) can perform worse than is accept(cid:173) able. In some cases, exploration might have unsafe, or even catas(cid:173) trophic, results, often modeled in terms of reaching'failure' states of the agent's environment. This paper presents a method that uses domain knowledge to reduce the number of failures during explo(cid:173) ration. This method formulates the set of actions from which the RL agent composes a control policy to ensure that exploration is conducted in a policy space that excludes most of the unacceptable policies. The resulting action set has a more abstract relationship to the task being solved than is common in many applications of RL.