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تسجيل مستخدم جديدAdaptive Lambda Least-Squares Temporal Difference Learning
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Temporal Difference learning or TD($lambda$) is a fundamental algorithm in the field of reinforcement learning. However, setting TDs $lambda$ parameter, which controls the timescale of TD updates, is generally left up to the practitioner. We formalize the $lambda$ selection problem as a bias-variance trade-off where the solution is the value of $lambda$ that leads to the smallest Mean Squared Value Error (MSVE). To solve this trade-off we suggest applying Leave-One-Trajectory-Out Cross-Validation (LOTO-CV) to search the space of $lambda$ values. Unfortunately, this approach is too computationally expensive for most practical applications. For Least Squares TD (LSTD) we show that LOTO-CV can be implemented efficiently to automatically tune $lambda$ and apply function optimization methods to efficiently search the space of $lambda$ values. The resulting algorithm, ALLSTD, is parameter free and our experiments demonstrate that ALLSTD is significantly computationally faster than the na{i}ve LOTO-CV implementation while achieving similar performance.
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We consider the core reinforcement-learning problem of on-policy value function approximation from a batch of trajectory data, and focus on various issues of Temporal Difference (TD) learning and Monte Carlo (MC) policy evaluation. The two methods ar
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This paper has been withdrawn by the author. This draft is withdrawn for its poor quality in english, unfortunately produced by the author when he was just starting his science route. Look at the ICML version instead: http://icml2008.cs.helsinki.fi/papers/111.pdf
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