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CORR
2016
Springer

Investigating practical, linear temporal difference learning

4 years 6 months ago
Investigating practical, linear temporal difference learning
Off-policy reinforcement learning has many applications including: learning from demonstration, learning multiple goal seeking policies in parallel, and representing predictive knowledge. Recently there has been an proliferation of new policyevaluation algorithms that fill a longstanding algorithmic void in reinforcement learning: combining robustness to offpolicy sampling, function approximation, linear complexity, and temporal difference (TD) updates. This paper contains two main contributions. First, we derive two new hybrid TD policy-evaluation algorithms, which fill a gap in this collection of algorithms. Second, we perform an empirical comparison to elicit which of these new linear TD methods should be preferred in different situations, and make concrete suggestions about practical use. Keywords Reinforcement learning; temporal difference learning; offpolicy learning
Adam M. White, Martha White
Added 31 Mar 2016
Updated 31 Mar 2016
Type Journal
Year 2016
Where CORR
Authors Adam M. White, Martha White
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