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Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result
Approximate dynamic programming approaches to the reinforcement learning problem are often categorized into greedy value function methods and value-based policy gradient methods.
A reinterpretation of the policy oscillation phenomenon in approximate policy iteration
We take a fresh view to this phenomenon by casting a considerable subset of the former approach as a limiting special case of the latter.
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