Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions
This work addresses the challenge of reward estimation in complex environments for researchers and practitioners in reinforcement learning, though it appears incremental as it builds on existing methods with specific modifications like anchor actions.
The paper tackles the problem of inverse reinforcement learning by proposing a framework called PQR to estimate reward functions using deep energy-based policies, allowing for action-dependent rewards and stochastic transitions, and demonstrates its performance on synthetic and real-world datasets with theoretical guarantees for known transitions and error bounds for unknown ones.
We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning. PQR does not assume that the reward solely depends on the state, instead it allows for a dependency on the choice of action. Moreover, PQR allows for stochastic state transitions. To accomplish this, we assume the existence of one anchor action whose reward is known, typically the action of doing nothing, yielding no reward. We present both estimators and algorithms for the PQR method. When the environment transition is known, we prove that the PQR reward estimator uniquely recovers the true reward. With unknown transitions, we bound the estimation error of PQR. Finally, the performance of PQR is demonstrated by synthetic and real-world datasets.