Nov 15, 1999 · We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such ...
Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of ...
We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function- ...
We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function- ...
Applies the results of a convergence theorem for a particular class of fixed-point computations that are particularly relevant to reinforcement learning, ...
A powerful new theorem is presented that can provide a unified analysis of value-function-based reinforcement-learning algorithms and allows the convergence ...
We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function- ...
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Dive into the research topics of 'A unified analysis of value-function-based reinforcement-learning algorithms'. Together they form a unique fingerprint. Sort ...
RLPy is a new reinforcement learning framework focused on value-function-based reinforce- ment learning using linear function approximation with discrete ...
A Unified Analysis of Value-Function-Based Reinforcement Learning Algorithms. Reinforcement learning is the problem of generating optimal behavior in a ...