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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 251–300 of 514 papers

TitleStatusHype
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distributionCode0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
A Langevin-like Sampler for Discrete DistributionsCode1
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
On Preemption and Learning in Stochastic SchedulingCode0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
Learning Math Reasoning from Self-Sampled Correct and Partially-Correct SolutionsCode1
Learning to Solve Combinatorial Graph Partitioning Problems via Efficient ExplorationCode1
Personalized Algorithmic Recourse with Preference ElicitationCode0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
The Sufficiency of Off-Policyness and Soft Clipping: PPO is still Insufficient according to an Off-Policy MeasureCode1
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees—0
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors—0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Think Global, Act Local: Dual-scale Graph Transformer for Vision-and-Language NavigationCode2
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
Online Decision TransformerCode2
Lagrangian Manifold Monte Carlo on Monge PatchesCode0
Efficient Policy Space Response Oracles—0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
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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