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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 7180 of 514 papers

TitleStatusHype
DrSR: LLM based Scientific Equation Discovery with Dual Reasoning from Data and Experience0
WoMAP: World Models For Embodied Open-Vocabulary Object Localization0
HelixDesign-Binder: A Scalable Production-Grade Platform for Binder Design Built on HelixFold30
DISCOVER: Automated Curricula for Sparse-Reward Reinforcement LearningCode0
STAR-R1: Spacial TrAnsformation Reasoning by Reinforcing Multimodal LLMsCode0
Comparative Analysis of Black-Box Optimization Methods for Weather Intervention Design0
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-TuningCode0
Distilling Realizable Students from Unrealizable Teachers0
Language Agents Mirror Human Causal Reasoning Biases. How Can We Help Them Think Like Scientists?0
Credit Assignment and Efficient Exploration based on Influence Scope in Multi-agent Reinforcement Learning0
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