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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

Showing 6170 of 514 papers

TitleStatusHype
Shared Experience Actor-Critic for Multi-Agent Reinforcement LearningCode1
Scaling MAP-Elites to Deep NeuroevolutionCode1
Optimistic Exploration even with a Pessimistic InitialisationCode1
Meta Reinforcement Learning with Autonomous Inference of Subtask DependenciesCode1
Self-Supervised Exploration via DisagreementCode1
Learning Exploration Policies for NavigationCode1
Model-Based Active ExplorationCode1
Automatic chemical design using a data-driven continuous representation of moleculesCode1
MOORL: A Framework for Integrating Offline-Online Reinforcement Learning0
Go-Browse: Training Web Agents with Structured Exploration0
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