SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

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Showing 10601–10625 of 15113 papers

TitleStatusHype
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Examining average and discounted reward optimality criteria in reinforcement learning—0
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks—0
Exchangeable Input Representations for Reinforcement Learning—0
Excluding the Irrelevant: Focusing Reinforcement Learning through Continuous Action Masking—0
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
Execute Order 66: Targeted Data Poisoning for Reinforcement Learning—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
Expected Policy Gradients for Reinforcement Learning—0
Expected Scalarised Returns Dominance: A New Solution Concept for Multi-Objective Decision Making—0
Experience Augmentation: Boosting and Accelerating Off-Policy Multi-Agent Reinforcement Learning—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Experience-driven Networking: A Deep Reinforcement Learning based Approach—0
Experience enrichment based task independent reward model—0
Experience Replay More When It's a Key Transition in Deep Reinforcement Learning—0
Experience Replay Optimization—0
Experience Replay Using Transition Sequences—0
Experience Sharing Between Cooperative Reinforcement Learning Agents—0
Experimental analysis of data-driven control for a building heating system—0
Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar—0
Experimental Evidence that Empowerment May Drive Exploration in Sparse-Reward Environments—0
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods—0
Experimental Study on Reinforcement Learning-based Control of an Acrobot—0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement Learning—0
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified