This paper presents postponed updates, a new strategy for TD methods,that can improve sample efficiency with- out incurring the computational and space requirements of model-based RL. By recording the agent\u2019s last-visit experi- ence, 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents postponed updates, a new strategy for TD methods,that can improve sample efficiency with- out incurring the computational and space requirements of model-based RL. By recording the agent\u2019s last-visit experi- ence, the agent can delay its update until the given state is revisited, thereby improving the quality of the update. Experimental results demonstrate,that […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-442698","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Ninth International Conference on Intelligent Systems Design and Applications, ISDA 2009, Pisa, 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