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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 126–150 of 514 papers

TitleStatusHype
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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