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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 211–220 of 514 papers

TitleStatusHype
Efficient Exploration in Continuous-time Model-based Reinforcement Learning—0
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Co-NavGPT: Multi-Robot Cooperative Visual Semantic Navigation Using Vision Language Models—0
Information Content Exploration—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks—0
Feature Interaction Aware Automated Data Representation TransformationCode0
DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems—0
Provably Efficient Exploration in Constrained Reinforcement Learning:Posterior Sampling Is All You Need—0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
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