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Reinforcement learning in complex environments may require supervision toprevent the agent from attempting dangerous actions. As a result of supervisorintervention, the executed action may differ from the action specified by thepolicy. How does this affect learning? We present the Modified-Action MarkovDecision Process, an extension of the MDP model that allows actions to differfrom the policy. We analyze the asymptotic behaviours of common reinforcementlearning algorithms in this setting and show that they adapt in different ways:some completely ignore modifications while others go to various lengths intrying to avoid action modifications that decrease reward. By choosing theright algorithm, developers can prevent their agents from learning tocircumvent interruptions or constraints, and better control agent responses toother kinds of action modification, like self-damage.

How RL Agents Behave When Their Actions Are Modified