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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 231–240 of 514 papers

TitleStatusHype
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
Comprehensive decision-strategy space exploration for efficient territorial planning strategies—0
Goal-oriented Trajectories for Efficient Exploration—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path—0
Go-Browse: Training Web Agents with Structured Exploration—0
Go-Explore for Residential Energy Management—0
GraphEQA: Using 3D Semantic Scene Graphs for Real-time Embodied Question Answering—0
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Efficient Exploration in Binary and Preferential Bayesian Optimization—0
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