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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 176–200 of 514 papers

TitleStatusHype
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need—0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments—0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects—0
Go Beyond Imagination: Maximizing Episodic Reachability with World ModelsCode0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Bag of Policies for Distributional Deep Exploration—0
Towards A Unified Agent with Foundation Models—0
LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search—0
Approximate information for efficient exploration-exploitation strategies—0
Improving Protein Optimization with Smoothed Fitness LandscapesCode1
Maximum State Entropy Exploration using Predecessor and Successor Representations—0
DISCO-10M: A Large-Scale Music Dataset—0
Inferring Hierarchical Structure in Multi-Room Maze Environments—0
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP—0
A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning—0
Tuning Legged Locomotion Controllers via Safe Bayesian OptimizationCode1
PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithm—0
Magnitude Attention-based Dynamic Pruning—0
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial OptimizationCode0
Large-Batch, Iteration-Efficient Neural Bayesian Design OptimizationCode0
Discovering Failure Modes of Text-guided Diffusion Models via Adversarial Search—0
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte CarloCode1
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