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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 2130 of 514 papers

TitleStatusHype
Evolutionary Large Language Model for Automated Feature TransformationCode1
Navigating Chemical Space with Latent FlowsCode1
Diffusion-Reinforcement Learning Hierarchical Motion Planning in Multi-agent Adversarial GamesCode1
MAMBA: an Effective World Model Approach for Meta-Reinforcement LearningCode1
Safe Guaranteed Exploration for Non-linear SystemsCode1
A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?Code1
Layered and Staged Monte Carlo Tree Search for SMT Strategy SynthesisCode1
PGDQN: Preference-Guided Deep Q-NetworkCode1
Improving Protein Optimization with Smoothed Fitness LandscapesCode1
Tuning Legged Locomotion Controllers via Safe Bayesian OptimizationCode1
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