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 1401–1410 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
Benchmarking Smoothness and Reducing High-Frequency Oscillations in Continuous Control Policies—0
Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning—0
Exploring RL-based LLM Training for Formal Language Tasks with Programmed RewardsCode0
DyPNIPP: Predicting Environment Dynamics for RL-based Robust Informative Path Planning—0
Curriculum Reinforcement Learning for Complex Reward Functions—0
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
Offline reinforcement learning for job-shop scheduling problems—0
Reinforced Imitative Trajectory Planning for Urban Automated DrivingCode1
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Benchmark Results

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