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Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL).
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Earlier work this paper cites.
Long short-term memory
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Earlier work this paper cites.
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