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 3081–3090 of 15113 papers

TitleStatusHype
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning—0
A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models—0
A Geometric Perspective on Optimal Representations for Reinforcement Learning—0
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions—0
A Survey of Reinforcement Learning Informed by Natural Language—0
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning—0
Query Rewriting for Effective Misinformation Discovery—0
Deep Q-Networks for Accelerating the Training of Deep Neural Networks—0
Deep Randomized Least Squares Value Iteration—0
A Survey of Reinforcement Learning from Human Feedback—0
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Benchmark Results

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