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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 461–470 of 514 papers

Title	Date	Tasks	Status
Lagrangian Manifold Monte Carlo on Monge Patches	Feb 1, 2022	Efficient Exploration	CodeCode Available
Efficient Gradient-Free Variational Inference using Policy Search	Jul 1, 2018	Efficient ExplorationVariational Inference	CodeCode Available
Efficient Exploration via State Marginal Matching	Jun 12, 2019	Efficient ExplorationReinforcement Learning	CodeCode Available
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement Learning	Apr 9, 2021	Deep Reinforcement LearningEfficient Exploration	CodeCode Available
Large-Batch, Iteration-Efficient Neural Bayesian Design Optimization	Jun 1, 2023	Bayesian OptimizationEfficient Exploration	CodeCode Available
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement Learning	Sep 8, 2022	Efficient Explorationreinforcement-learning	CodeCode Available
Concurrent Meta Reinforcement Learning	Mar 7, 2019	Efficient ExplorationMeta-Learning	CodeCode Available
Efficient Exploration through Bayesian Deep Q-Networks	Feb 13, 2018	Atari GamesEfficient Exploration	CodeCode Available
Efficient Exploration of the Rashomon Set of Rule Set Models	Jun 5, 2024	Decision MakingEfficient Exploration	CodeCode Available
Amortized Variational Deep Q Network	Nov 3, 2020	Deep Reinforcement LearningEfficient Exploration	CodeCode Available

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