Speeding-up Reinforcement Learning with
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Speeding up Tabular Reinforcement Learning Using State-Action Similarities
One of the most prominent approaches for speeding up reinforcement learning is injecting human prior knowledge into the learning agent. This paper proposes a novel method to speed up temporal difference learning by using state-action similarities. These handcoded similarities are tested in three well-studied domains of varying complexity, demonstrating our approach’s benefits.
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One of the most prominent approaches for speeding up reinforcement learning is injecting human prior knowledge into the learning agent. This paper proposes a novel method to speed up temporal difference learning by using state-action similarities. These handcoded similarities are tested in three well-studied domains of varying complexity, demonstrating our approach’s benefits.
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Reinforcement learning (RL) methods (Sutton & Barto 1998) have become popular machine learning techniques in recent years. RL has had some experimental successes and has been shown to exhibit some desirable properties in theory, but it has often been found very slow in practice. In this paper we introduce behavior transfer, a novel approach to speeding up traditional RL. We present experimental...
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تاریخ انتشار 2001