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 3551–3575 of 15113 papers

TitleStatusHype
Imitating Graph-Based Planning with Goal-Conditioned PoliciesCode1
Deceptive Reinforcement Learning in Model-Free Domains—0
DataLight: Offline Data-Driven Traffic Signal ControlCode1
A Survey of Demonstration Learning—0
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs—0
Multi-modal reward for visual relationships-based image captioning—0
Active hypothesis testing in unknown environments using recurrent neural networks and model free reinforcement learning—0
Reinforcement Learning Friendly Vision-Language Model for MinecraftCode1
Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning—0
Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning—0
Hybrid Systems Neural Control with Region-of-Attraction Planner—0
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management—0
Comparing NARS and Reinforcement Learning: An Analysis of ONA and Q-Learning Algorithms—0
Measurement Optimization under Uncertainty using Deep Reinforcement Learning—0
Optimal Horizon-Free Reward-Free Exploration for Linear Mixture MDPs—0
Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control—0
Dynamic Update-to-Data Ratio: Minimizing World Model OverfittingCode0
A Data-Driven Model-Reference Adaptive Control Approach Based on Reinforcement Learning—0
A New Policy Iteration Algorithm For Reinforcement Learning in Zero-Sum Markov Games—0
Online Reinforcement Learning in Periodic MDP—0
Psychotherapy AI Companion with Reinforcement Learning Recommendations and Interpretable Policy Dynamics—0
Efficient Learning of High Level Plans from Play—0
Reinforcement Learning for Omega-Regular Specifications on Continuous-Time MDP—0
Self-Inspection Method of Unmanned Aerial Vehicles in Power Plants Using Deep Q-Network Reinforcement Learning—0
Recommending the optimal policy by learning to act from temporal data—0
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Benchmark Results

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