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Found 6 papers, 2 papers with code
Provable Hierarchy-Based Meta-Reinforcement Learning
To address this gap, we analyze HRL in the meta-RL setting, where a learner learns latent hierarchical structure during meta-training for use in a downstream task.
How Fine-Tuning Allows for Effective Meta-Learning
Representation learning has been widely studied in the context of meta-learning, enabling rapid learning of new tasks through shared representations.
On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning
We demonstrate that this problem can be tackled effectively with automated HPO, which we demonstrate to yield significantly improved performance compared to human experts.
Hyperparameter Optimization
Model-based Reinforcement Learning
+2
Unsupervised Exploration with Deep Model-Based Reinforcement Learning
We show that both challenges can be addressed by representing model-uncertainty, which can both guide exploration in the unsupervised phase and ensure that the errors in the model are not exploited by the planner in the goal-directed phase.
Model-based Reinforcement Learning
reinforcement-learning
+1
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models
Model-based reinforcement learning (RL) algorithms can attain excellent sample efficiency, but often lag behind the best model-free algorithms in terms of asymptotic performance.
Model-based Reinforcement Learning
reinforcement-learning
+1
MBMF: Model-Based Priors for Model-Free Reinforcement Learning
Reinforcement Learning is divided in two main paradigms: model-free and model-based.
