Boosting Soft Q-Learning by Bounding

An agent’s ability to leverage past experience is critical for efficiently solving new tasks. Prior work has focused on using value function estimates to obtain zero-shot approximations for solutions to a new task. In soft $Q$-learning, we show how any value function estimate can also be used to derive double-sided bounds on the optimal value function. The derived bounds lead to new approaches for boosting training performance which we validate experimentally. Notably, we find that the proposed framework suggests an alternative method for updating the $Q$-function, leading to boosted performance.

Authors

• Jacob Adamczyk University of Massachusetts Boston The NSF Institute for Artificial Intelligence and Fundamental Interactions
• Volodymyr Makarenko San Jose State University
• Stas Tiomkin Texas Tech University
• Rahul V Kulkarni

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