Machine Learning - Special issue on reinforcement learning
Finite-sample convergence rates for Q-learning and indirect algorithms
Proceedings of the 1998 conference on Advances in neural information processing systems II
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Near-Optimal Reinforcement Learning in Polynominal Time
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning in embedded systems
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
The Journal of Machine Learning Research
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While direct, model-free reinforcement learning often performs better than model-based approaches in practice, only the latter have yet supported theoretical guarantees for finite-sample convergence. A major difficulty in analyzing the direct approach in an online setting is the absence of a definitive exploration strategy. We extend the notion of admissibility to direct reinforcement learning and show that standard Q-learning with optimistic initial values and constant learning rate is admissible. The notion justifies the use of a greedy strategy that we believe performs very well in practice and holds theoretical significance in deriving finite-sample convergence for direct reinforcement learning. We present empirical evidence that supports our idea.