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Found 3 papers, 1 papers with code
Same State, Different Task: Continual Reinforcement Learning without Interference
In this paper we formalize this "interference" as distinct from the problem of forgetting.
Revisiting Design Choices in Offline Model Based Reinforcement Learning
Offline reinforcement learning enables agents to make use of large pre-collected datasets of environment transitions and learn control policies without the need for potentially expensive or unsafe online data collection.
Ready Policy One: World Building Through Active Learning
Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks.
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