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 3131–3140 of 15113 papers

TitleStatusHype
Bridging the Reality Gap of Reinforcement Learning based Traffic Signal Control using Domain Randomization and Meta Learning—0
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
Reparameterized Policy Learning for Multimodal Trajectory Optimization—0
Explaining Autonomous Driving Actions with Visual Question AnsweringCode1
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop GamesCode1
A reinforcement learning approach for VQA validation: an application to diabetic macular edema grading—0
Benchmarking Potential Based Rewards for Learning Humanoid LocomotionCode2
Towards A Unified Agent with Foundation Models—0
Distributed 3D-Beam Reforming for Hovering-Tolerant UAVs Communication over Coexistence: A Deep-Q Learning for Intelligent Space-Air-Ground Integrated Networks—0
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading—0
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Benchmark Results

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