Web: http://arxiv.org/abs/2209.06356

Sept. 15, 2022, 1:11 a.m. | Augustine N. Mavor-Parker, Andrea Banino, Lewis D. Griffin, Caswell Barry

cs.LG updates on arXiv.org arxiv.org

Animals are able to rapidly infer from limited experience when sets of state
action pairs have equivalent reward and transition dynamics. On the other hand,
modern reinforcement learning systems must painstakingly learn through trial
and error that sets of state action pairs are value equivalent -- requiring an
often prohibitively large amount of samples from their environment. MDP
homomorphisms have been proposed that reduce the observed MDP of an environment
to an abstract MDP, which can enable more sample efficient …

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