Regret Bounds for Reinforcement Learning with Policy Advice

5 May 2013Mohammad Gheshlaghi AzarAlessandro LazaricEmma Brunskill

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand... (read more)

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