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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 101–150 of 514 papers

TitleStatusHype
Batch Bayesian Optimization via Local PenalizationCode0
Curiosity Driven Exploration of Learned Disentangled Goal SpacesCode0
Learning Dynamic Cognitive Map with Autonomous NavigationCode0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
Balancing Value Underestimation and Overestimation with Realistic Actor-CriticCode0
Count-Based Exploration with the Successor RepresentationCode0
Count-Based Exploration in Feature Space for Reinforcement LearningCode0
Learning-Driven Exploration for Reinforcement LearningCode0
Behavior-Guided Actor-Critic: Improving Exploration via Learning Policy Behavior Representation for Deep Reinforcement LearningCode0
Multi-Objective Hyperparameter Selection via Hypothesis Testing on Reliability GraphsCode0
Learning to Act with Affordance-Aware Multimodal Neural SLAMCode0
A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextCode0
Better Exploration with Optimistic Actor CriticCode0
Large-Batch, Iteration-Efficient Neural Bayesian Design OptimizationCode0
Lagrangian Manifold Monte Carlo on Monge PatchesCode0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Instance Temperature Knowledge DistillationCode0
A Gradient Sampling Algorithm for Stratified Maps with Applications to Topological Data AnalysisCode0
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapesCode0
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
Discovering and Exploiting Sparse Rewards in a Learned Behavior SpaceCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Disentangling Uncertainties by Learning Compressed Data RepresentationCode0
Learning to Seek: Autonomous Source Seeking with Deep Reinforcement Learning Onboard a Nano Drone MicrocontrollerCode0
OTO Planner: An Efficient Only Travelling Once Exploration Planner for Complex and Unknown EnvironmentsCode0
ConEx: Efficient Exploration of Big-Data System Configurations for Better PerformanceCode0
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement LearningCode0
A Fast and Scalable Polyatomic Frank-Wolfe Algorithm for the LASSOCode0
Concurrent Meta Reinforcement LearningCode0
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgentCode0
Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic SystemsCode0
Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and GeneralizationCode0
Hierarchical Spatial Proximity Reasoning for Vision-and-Language NavigationCode0
GenPlan: Generative Sequence Models as Adaptive PlannersCode0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and ExplorationsCode0
Efficient Exploration in Average-Reward Constrained Reinforcement Learning: Achieving Near-Optimal Regret With Posterior SamplingCode0
Model-based Reinforcement Learning for Continuous Control with Posterior SamplingCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Few-shot_LLM_Synthetic_Data_with_Distribution_MatchingCode0
Federated Control with Hierarchical Multi-Agent Deep Reinforcement LearningCode0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
Exploring through Random Curiosity with General Value FunctionsCode0
Exploratory State Representation LearningCode0
Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal GuidanceCode0
Go Beyond Imagination: Maximizing Episodic Reachability with World ModelsCode0
EXPODE: EXploiting POlicy Discrepancy for Efficient Exploration in Multi-agent Reinforcement LearningCode0
Dynamic Subgoal-based Exploration via Bayesian OptimizationCode0
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