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Exploration in Feature Space for Reinforcement Learning
[article]
2017
arXiv
pre-print
The infamous exploration-exploitation dilemma is one of the oldest and most important problems in reinforcement learning (RL). Deliberate and effective exploration is necessary for RL agents to succeed in most environments. However, until very recently even very sophisticated RL algorithms employed simple, undirected exploration strategies in large-scale RL tasks. We introduce a new optimistic count-based exploration algorithm for RL that is feasible in high-dimensional MDPs. The success of RL
arXiv:1710.02210v1
fatcat:7ddmu3kjdjd6hkoqjfdz375cii