Adaptive Policy Transfer in Reinforcement Learning
This addresses the problem of robust policy transfer for real-world robotics, offering an incremental improvement over existing methods.
The paper tackles the challenge of efficient policy transfer in reinforcement learning by introducing an 'Adapt-to-Learn' mechanism that combines adaptation and exploration, resulting in significantly reduced sample complexity for transferring skills between related tasks.
Efficient and robust policy transfer remains a key challenge for reinforcement learning to become viable for real-wold robotics. Policy transfer through warm initialization, imitation, or interacting over a large set of agents with randomized instances, have been commonly applied to solve a variety of Reinforcement Learning tasks. However, this seems far from how skill transfer happens in the biological world: Humans and animals are able to quickly adapt the learned behaviors between similar tasks and learn new skills when presented with new situations. Here we seek to answer the question: Will learning to combine adaptation and exploration lead to a more efficient transfer of policies between domains? We introduce a principled mechanism that can "Adapt-to-Learn", that is adapt the source policy to learn to solve a target task with significant transition differences and uncertainties. We show that the presented method learns to seamlessly combine learning from adaptation and exploration and leads to a robust policy transfer algorithm with significantly reduced sample complexity in transferring skills between related tasks.