Efficient learning in linearly solvable MDP models
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Wireless Personal Communications: An International Journal
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This paper presents postponed updates, a new strategy for TD methods that can improve sample efficiency without incurring the computational and space requirements of model-based RL. By recording the agent's last-visit experience, the agent can delay its update until the given state is revisited, thereby improving the quality of the update. Experimental results demonstrate that postponed updates outperforms several competitors, most notably eligibility traces, a traditional way to improve the sample efficiency of TD methods. It achieves this without the need to tune an extra parameter as is needed for eligibility traces.