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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

Title	Date	Tasks	Status	Hype
Estimating Risk and Uncertainty in Deep Reinforcement Learning	May 23, 2019	Bayesian InferenceDeep Reinforcement Learning	CodeCode Available	0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning	May 16, 2019	Efficient ExplorationMeta Reinforcement Learning	—Unverified	0
Distributional Reinforcement Learning for Efficient Exploration	May 13, 2019	Atari GamesDistributional Reinforcement Learning	—Unverified	0
Optimizing Routerless Network-on-Chip Designs: An Innovative Learning-Based Framework	May 11, 2019	Deep Reinforcement LearningEfficient Exploration	—Unverified	0
Explicit Recall for Efficient Exploration	May 1, 2019	Decision MakingDeep Reinforcement Learning	—Unverified	0
Beyond Games: Bringing Exploration to Robots in Real-world	May 1, 2019	Efficient ExplorationReinforcement Learning	—Unverified	0
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors	Apr 22, 2019	Decision MakingDeep Reinforcement Learning	CodeCode Available	0
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables	Mar 19, 2019	Deep Reinforcement LearningEfficient Exploration	CodeCode Available	0
Concurrent Meta Reinforcement Learning	Mar 7, 2019	Efficient ExplorationMeta-Learning	CodeCode Available	0
Learning Exploration Policies for Navigation	Mar 5, 2019	Efficient ExplorationGeneral Reinforcement Learning	CodeCode Available	1

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