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Found 3 papers, 1 papers with code
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems.
Reusable Options through Gradient-based Meta Learning
Subsequently, we formulate the desiderata for reusable options and use these to frame the problem of learning options as a gradient-based meta-learning problem.
Meta Reinforcement Learning for Fast Adaptation of Hierarchical Policies
Hierarchical methods have the potential to allow reinforcement learning to scale to larger environments.
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