Distributional Bellman Operators over Mean Embeddings
CoRR(2023)
摘要
We propose a novel algorithmic framework for distributional reinforcement
learning, based on learning finite-dimensional mean embeddings of return
distributions. We derive several new algorithms for dynamic programming and
temporal-difference learning based on this framework, provide asymptotic
convergence theory, and examine the empirical performance of the algorithms on
a suite of tabular tasks. Further, we show that this approach can be
straightforwardly combined with deep reinforcement learning, and obtain a new
deep RL agent that improves over baseline distributional approaches on the
Arcade Learning Environment.
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