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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 411–420 of 514 papers

TitleStatusHype
Learning Index Selection with Structured Action Spaces—0
Biased Estimates of Advantages over Path Ensembles—0
n-Regret for Learning in Markov Decision Processes with Function Approximation and Low Bellman Rank—0
Improving a State-of-the-Art Heuristic for the Minimum Latency Problem with Data Mining—0
Learning to Explore in Motion and Interaction Tasks—0
Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards—0
Directed Exploration for Reinforcement Learning—0
Learning-Driven Exploration for Reinforcement LearningCode0
Efficient Exploration via State Marginal MatchingCode0
Learning to Score Behaviors for Guided Policy OptimizationCode0
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