Universal Agent for Disentangling Environments and Tasks

international conference on learning representations, 2018.

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Abstract:

Recent state-of-the-art reinforcement learning algorithms are trained under the goal of excelling in one specific task. Hence, both environment and task specific knowledge are entangled into one framework. However, there are often scenarios where the environment (e.g. the physical world) is fixed while only the target task changes. Hence,...More

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