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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 421–430 of 514 papers

TitleStatusHype
Worst-Case Regret Bounds for Exploration via Randomized Value Functions—0
Clustered Reinforcement Learning—0
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy—0
Estimating Risk and Uncertainty in Deep Reinforcement LearningCode0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning—0
Distributional Reinforcement Learning for Efficient Exploration—0
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework—0
Beyond Games: Bringing Exploration to Robots in Real-world—0
Explicit Recall for Efficient Exploration—0
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human PriorsCode0
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