Eligibility traces and forgetting factor in recursive least‐squares‐based temporal difference
نویسندگان
چکیده
منابع مشابه
Experimental analysis of eligibility traces strategies in temporal difference learning
Temporal difference (TD) learning is a model-free reinforcement learning technique, which adopts an infinite horizon discount model and uses an incremental learning technique for dynamic programming. The state value function is updated in terms of sample episodes. Utilising eligibility traces is a key mechanism in enhancing the rate of convergence. TD(λ) represents the use of eligibility traces...
متن کاملTD(λ) Networks: Temporal-Difference Networks with Eligibility Traces
Temporal-difference (TD) networks have been introduced as a formalism for expressing and learning grounded world knowledge in a predictive form (Sutton & Tanner, 2005). Like conventional TD(0) methods, the learning algorithm for TD networks uses 1-step backups to train prediction units about future events. In conventional TD learning, the TD(λ) algorithm is often used to do more general multi-s...
متن کاملRecursive Least-Squares Learning with Eligibility Traces
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 کاملTemporal Second Difference Traces
Q-learning is a reliable but inefficient off-policy temporal-difference method, backing up reward only one step at a time. Replacing traces, using a recency heuristic, are more efficient but less reliable. In this work, we introduce model-free, off-policy temporal difference methods that make better use of experience than Watkins’ Q(λ). We introduce both Optimistic Q(λ) and the temporal second ...
متن کاملA Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
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...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Adaptive Control and Signal Processing
سال: 2021
ISSN: ['0890-6327', '1099-1115']
DOI: https://doi.org/10.1002/acs.3282