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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 276–300 of 514 papers

TitleStatusHype
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesisCode0
Using Non-Stationary Bandits for Learning in Repeated Cournot Games with Non-Stationary Demand—0
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
BooVI: Provably Efficient Bootstrapped Value Iteration—0
NovelD: A Simple yet Effective Exploration CriterionCode1
HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space—0
IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions—0
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven ExplorationCode1
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Bayesian optimization of distributed neurodynamical controller models for spatial navigation—0
Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives—0
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
Landmark-Guided Subgoal Generation in Hierarchical Reinforcement LearningCode1
Map Induction: Compositional spatial submap learning for efficient exploration in novel environmentsCode0
Hierarchical Skills for Efficient ExplorationCode1
More Efficient Exploration with Symbolic Priors on Action Sequence Equivalences—0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
Efficient Exploration in Binary and Preferential Bayesian Optimization—0
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization—0
Reinforcement Learning in Reward-Mixing MDPs—0
Divide and Explore: Multi-Agent Separate Exploration with Shared Intrinsic Motivations—0
Learning to Solve Combinatorial Problems via Efficient Exploration—0
HyperDQN: A Randomized Exploration Method for Deep Reinforcement LearningCode1
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