VSCL Graduate Research Assistant Vinicius Goecks will present a paper on “Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments” at the International Conference on Autonomous Agents and Multi-Agent Systems on May 11, 2020. Co-authored by researchers from the US Army Research Laboratory’s Human Research and Engineering Directorate, this continuing project investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. This method outperforms state-of-the-art techniques for combining behavior cloning and reinforcement learning for both dense and sparse reward scenarios. Results also suggest that directly including the behavior cloning loss on demonstration data helps to ensure stable learning and ground future policy updates.
The paper documenting this work, “Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments,” is available at the official AAMAS 2020 proceedings, together with the supplemental material detailing the training hyperparameters.