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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6801–6825 of 15113 papers

TitleStatusHype
Tractable Representations for Convergent Approximation of Distributional HJB Equations—0
TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution—0
Trading the Twitter Sentiment with Reinforcement Learning—0
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning—0
Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learning—0
Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing—0
Traffic Signal Control with Communicative Deep Reinforcement Learning Agents: a Case Study—0
Train a snake with reinforcement learning algorithms—0
Training a Constrained Natural Media Painting Agent using Reinforcement Learning—0
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models—0
Training Characteristic Functions with Reinforcement Learning: XAI-methods play Connect Four—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference—0
Training in Task Space to Speed Up and Guide Reinforcement Learning—0
Training Language Models to Critique With Multi-agent Feedback—0
Training Large Language Models to Reason via EM Policy Gradient—0
Training Larger Networks for Deep Reinforcement Learning—0
Training like Playing: A Reinforcement Learning And Knowledge Graph-based framework for building Automatic Consultation System in Medical Field—0
LeDex: Training LLMs to Better Self-Debug and Explain Code—0
Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators—0
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior—0
Trajectory-based Learning for Ball-in-Maze Games—0
Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning—0
Trajectory Data Suffices for Statistically Efficient Learning in Offline RL with Linear q^π-Realizability and Concentrability—0
Trajectory First: A Curriculum for Discovering Diverse Policies—0
Show:102550
← PrevPage 273 of 605Next →

Benchmark Results

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