Reinforcement learning with replacing eligibility traces
نویسندگان
چکیده
منابع مشابه
Reinforcement Learning with Replacing Eligibility
The eligibility trace is one of the basic mechanisms used in reinforcement learning to handle delayed reward. In this paper we introduce a new kind of eligibility trace, the replacing trace, analyze it theoretically, and show that it results in faster, more reliable learning than the conventional trace. Both kinds of trace assign credit to prior events according to how recently they occurred, b...
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Recently, a new multi-step temporal learning algorithm, called Q(σ), unifies n-step Tree-Backup (when σ = 0) and n-step Sarsa (when σ = 1) by introducing a sampling parameter σ. However, similar to other multi-step temporal-difference learning algorithms, Q(σ) needs much memory consumption and computation time. Eligibility trace is an important mechanism to transform the off-line updates into e...
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In the framework of Markov Decision Processes, we consider the problem of learning a linear approximation of the value function of some fixed policy from one trajectory possibly generated by some other policy. We describe a systematic approach for adapting on-policy learning least squares algorithms of the literature (LSTD [5], LSPE [15], FPKF [7] and GPTD [8]/KTD [10]) to off-policy learning w...
متن کاملAn Analysis of Actor/Critic Algorithms Using Eligibility Traces: Reinforcement Learning with Imperfect Value Function
We present an analysis of actor/critic algorithms, in which the actor updates its policy using eligibility traces of the policy parameters. Most of the theoretical results for eligibility traces have been for only critic's value iteration algorithms. This paper investigates what the actor's eligibility trace does. The results show that the algorithm is an extension of Williams' REINFORCE algori...
متن کاملReplacing 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 pap...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1996
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00114726