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ESANN
2007

Replacing eligibility trace for action-value learning with function approximation

8 years 12 months ago
Replacing eligibility trace for action-value learning with function approximation
The eligibility trace is one of the most used mechanisms to speed up reinforcement learning. Earlier reported experiments seem to indicate that replacing eligibility traces would perform better than accumulating eligibility traces. However, replacing traces are currently not applicable when using function approximation methods where states are not represented uniquely by binary values. This paper proposes two modifications to replacing traces that overcome this limitation. Experimental results from the Mountain-Car task indicate that the new replacing traces outperform both the accumulating and the ‘ordinary’ replacing traces.
Kary Främling
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Kary Främling
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