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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

Showing 141150 of 514 papers

TitleStatusHype
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Exploring through Random Curiosity with General Value FunctionsCode0
Exploratory State Representation LearningCode0
Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal GuidanceCode0
Go Beyond Imagination: Maximizing Episodic Reachability with World ModelsCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
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