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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 491500 of 514 papers

TitleStatusHype
Bootstrapped Meta-LearningCode0
TOP-ERL: Transformer-based Off-Policy Episodic Reinforcement LearningCode0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Playing Text-Adventure Games with Graph-Based Deep Reinforcement LearningCode0
LECO: Learnable Episodic Count for Task-Specific Intrinsic RewardCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Bayesian Curiosity for Efficient Exploration in Reinforcement LearningCode0
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesisCode0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
Deep Exploration via Bootstrapped DQNCode0
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