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 1401–1425 of 15113 papers

TitleStatusHype
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning—0
Meta Stackelberg Game: Robust Federated Learning against Adaptive and Mixed Poisoning Attacks—0
Exploring RL-based LLM Training for Formal Language Tasks with Programmed RewardsCode0
Curriculum Reinforcement Learning for Complex Reward Functions—0
Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies—0
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
Reinforced Imitative Trajectory Planning for Urban Automated DrivingCode1
Offline reinforcement learning for job-shop scheduling problems—0
Reinforcement Learning for Dynamic Memory AllocationCode0
Training Language Models to Critique With Multi-agent Feedback—0
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning—0
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Action abstractions for amortized sampling—0
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Reinforcement Learning in Non-Markov Market-Making—0
Towards Effective Planning Strategies for Dynamic Opinion NetworksCode0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents—0
Harnessing Causality in Reinforcement Learning With Bagged Decision Times—0
Streaming Deep Reinforcement Learning Finally WorksCode3
Sliding Puzzles Gym: A Scalable Benchmark for State Representation in Visual Reinforcement LearningCode1
Coordinated Dispatch of Energy Storage Systems in the Active Distribution Network: A Complementary Reinforcement Learning and Optimization Approach—0
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Benchmark Results

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