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Rollout sampling approximate policy iteration
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DOI：10.1007/s10994-008-5069-3.png)
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摘要
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Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.
Keyword:
reinforcement learning
approximate policy iteration
rollouts
bandit problems
classification
sample complexity
期刊
IF:
2.9
论文数:
2.7K
被引数:
3.4W
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引用论文
Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path基于Bellman残差最小化的拟合策略迭代和单样本路径学习接近最优的策略.png)
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