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Combining Counterfactual Regret Minimization with Information Gain to Solve Extensive Games with Imperfect Information
[article]
2021
arXiv
pre-print
Counterfactual regret Minimization (CFR) is an effective algorithm for solving extensive games with imperfect information (IIEG). However, CFR is only allowed to apply in a known environment such as the transition functions of the chance player and reward functions of the terminal nodes are aware in IIEGs. For uncertain scenarios like the cases under Reinforcement Learning (RL), variational information maximizing exploration (VIME) provides a useful framework for exploring environments using
arXiv:2110.07892v1
fatcat:xopyt5kxmvhbbo4tlvwpoegsnu