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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 201–225 of 514 papers

TitleStatusHype
Go-Explore for Residential Energy Management—0
Mutual Enhancement of Large Language and Reinforcement Learning Models through Bi-Directional Feedback Mechanisms: A Case Study—0
Diffusion-ES: Gradient-free Planning with Diffusion for Autonomous and Instruction-guided Driving—0
A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration—0
TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly DetectionCode0
Joint channel estimation and data detection in massive MIMO systems based on diffusion models—0
Consensus-based adaptive sampling and approximation for high-dimensional energy landscapesCode0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration—0
Visual Analytics for Efficient Image Exploration and User-Guided Image Captioning—0
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
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
Information Content Exploration—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
Learning Spatial and Temporal Hierarchies: Hierarchical Active Inference for navigation in Multi-Room Maze Environments—0
Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects—0
Go Beyond Imagination: Maximizing Episodic Reachability with World ModelsCode0
Reinforcement learning informed evolutionary search for autonomous systems testing—0
Bag of Policies for Distributional Deep Exploration—0
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