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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 376–400 of 514 papers

TitleStatusHype
JueWu-MC: Playing Minecraft with Sample-efficient Hierarchical Reinforcement Learning—0
KEA: Keeping Exploration Alive by Proactively Coordinating Exploration Strategies—0
K-Means Clustering using Tabu Search with Quantized Means—0
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?—0
Large-scale signatures of unconsciousness are consistent with a departure from critical dynamics—0
Latent Action Priors for Locomotion with Deep Reinforcement Learning—0
Learn2Hop: Learned Optimization on Rough Landscapes—0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
Learning Efficient and Effective Exploration Policies with Counterfactual Meta Policy—0
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees—0
Learning Exploration Policies for Model-Agnostic Meta-Reinforcement Learning—0
Learning Index Selection with Structured Action Spaces—0
Learning Memory-Dependent Continuous Control from Demonstrations—0
Learning Off-policy with Model-based Intrinsic Motivation For Active Online Exploration—0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Learning to Score Behaviors for Guided Policy OptimizationCode0
Meta-Learning for Stochastic Gradient MCMCCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Meta-Learning Integration in Hierarchical Reinforcement Learning for Advanced Task ComplexityCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
A diversity-enhanced genetic algorithm for efficient exploration of parameter spacesCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Count-Based Exploration with the Successor RepresentationCode0
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