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 1791–1800 of 15113 papers

TitleStatusHype
Deep reinforcement learning with symmetric data augmentation applied for aircraft lateral attitude tracking control—0
Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods—0
Communication-Aware Reinforcement Learning for Cooperative Adaptive Cruise Control—0
A Benchmark Environment for Offline Reinforcement Learning in Racing GamesCode1
Transductive Active Learning with Application to Safe Bayesian OptimizationCode1
PID Accelerated Temporal Difference Algorithms—0
Enhancing Performance and User Engagement in Everyday Stress Monitoring: A Context-Aware Active Reinforcement Learning Approach—0
A Review of Nine Physics Engines for Reinforcement Learning Research—0
Gradient Boosting Reinforcement LearningCode2
Token-Mol 1.0: Tokenized drug design with large language model—0
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Benchmark Results

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