A Conservative Approach for Few-Shot Transfer in Off-Dynamics Reinforcement Learning

Understand

Off-dynamics Reinforcement Learning (ODRL) seeks to transfer a policy from a source environment to a target environment characterized by distinct yet similar dynamics.

• In this context, traditional RL agents depend excessively on the dynamics of the source environment, resulting in the discovery of policies that excel in this environment but fail to provide reasonable performance in the target one.
• In the few-shot framework, a limited number of transitions from the target environment are introduced to facilitate a more effective transfer.
• Addressing this challenge, we propose an innovative approach inspired by recent advancements in Imitation Learning and conservative RL algorithms.