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The loss from imperfect value functions in expectation-based and minimax-based tasks

https://doi.org/10.1007/bf00114728
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Practical issues in temporal difference learning
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Q-learning
inferredcites →https://doi.org/10.1007/bf00993308openalex-snapshot
An Upper Bound on the Loss from Approximate Optimal-Value Functions
inferredcites →https://doi.org/10.1007/bf00993591openalex-snapshot
The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces
inferredcites →https://doi.org/10.1007/springerreference_178956openalex-snapshot
Efficient Exploration in Reinforcement Learning
inferredcites →https://doi.org/10.1016/b978-1-55860-307-3.50030-7openalex-snapshot
Scaling Up Reinforcement Learning for Robot Control
inferredcites →https://doi.org/10.1016/b978-1-55860-307-3.50045-9openalex-snapshot
A Reinforcement Learning Method for Maximizing Undiscounted Rewards
inferredcites →https://doi.org/10.1016/b978-1-55860-335-6.50021-0openalex-snapshot
Consideration of Risk in Reinforcement Learning
inferredcites →https://doi.org/10.1016/b978-1-55860-335-6.50027-1openalex-snapshot
Markov games as a framework for multi-agent reinforcement learning
inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50013-xopenalex-snapshot
Residual Algorithms: Reinforcement Learning with Function Approximation
inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50040-2openalex-snapshot
Stable Function Approximation in Dynamic Programming
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1023/a:1022624705476openalex-snapshot
Practical Issues in Temporal Difference Learning
inferredcites →https://doi.org/10.1023/a:1022656217772openalex-snapshot
The Parti-game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-spaces
inferredcites →https://doi.org/10.1023/a:1022689125041openalex-snapshot
Asynchronous Stochastic Approximation and Q-Learning
inferredcites →https://doi.org/10.1109/cdc.1993.325119openalex-snapshot
Asynchronous stochastic approximation and Q-learning
inferredcites →https://doi.org/10.1109/tsmc.1983.6313077openalex-snapshot
Neuronlike adaptive elements that can solve difficult learning control problems
inferredcites →https://doi.org/10.17863/cam.132164openalex-snapshot
Learning from Delayed Rewards
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Applied Probability Models with Optimization Applications.
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Operations Research -- An Introduction
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The Learning Machines

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms
inferred← citeshttps://doi.org/10.1007/bf00114729openalex-snapshot
The effect of representation and knowledge on goal-directed exploration with reinforcement-learning algorithms
inferred← citeshttps://doi.org/10.1016/s0004-3702(01)00103-5openalex-snapshot
Minimax real-time heuristic search
inferred← citeshttps://doi.org/10.1016/s0921-8890(00)00087-7openalex-snapshot
Dynamic job-shop scheduling using reinforcement learning agents
inferred← citeshttps://doi.org/10.1023/a:1007440607681openalex-snapshot
Module-Based Reinforcement Learning: Experiments with a Real Robot
inferred← citeshttps://doi.org/10.1023/a:1008858222277openalex-snapshot
Module-Based Reinforcement Learning: Experiments with a Real Robot
inferred← citeshttps://doi.org/10.1109/cdc.1998.758445openalex-snapshot
Minimax-based reinforcement learning with state aggregation
inferred← citeshttps://doi.org/10.1109/isic.2002.1157857openalex-snapshot
Residual-gradient-based neural reinforcement learning for the optimal control of an acrobot
inferred← citeshttps://doi.org/10.1109/nnsp.1997.622403openalex-snapshot
Adaptive control in anaesthesia
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Learning Control of Dynamical Systems Based on Markov Decision Processes: Research Frontiers and Outlooks
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