Fitted Q-iteration by Advantage Weighted RegressionGerhard Neumann; Jan Peters
In: Daphne Koller; Dale Schuurmans; Yoshua Bengio; Léon Bottou (Hrsg.). Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems. Neural Information Processing Systems (NeurIPS-2008), December 8-11, Vancouver, British Columbia, Canada, Pages 1177-1184, Curran Associates, Inc. 2008.
Recently, fitted Q-iteration (FQI) based methods have become more popular due to their increased sample efficiency, a more stable learning process and the higher quality of the resulting policy. However, these methods remain hard to use for continuous action spaces which frequently occur in real-world tasks, eg, in robotics and other technical applications. The greedy action selection commonly used for the policy improvement step is particularly problematic as it is expensive for continuous actions, can cause an unstable learning process, introduces an optimization bias and results in highly non-smooth policies unsuitable for real-world systems. In this paper, we show that by using a soft-greedy action selection the policy improvement step used in FQI can be simplified to an inexpensive advantage-weighted regression. With this result, we are able to derive a new, computationally efficient FQI algorithm which can even deal with high dimensional action spaces.