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 2711–2720 of 15113 papers

TitleStatusHype
ToolRL: Reward is All Tool Learning NeedsCode0
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent RegimeCode0
pix2pockets: Shot Suggestions in 8-Ball Pool from a Single Image in the Wild—0
Evolutionary Reinforcement Learning for Interpretable Decision-Making in Supply Chain Management—0
Revealing Covert Attention by Analyzing Human and Reinforcement Learning Agent Gameplay—0
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling—0
Next-Future: Sample-Efficient Policy Learning for Robotic-Arm Tasks—0
Hallucination-Aware Generative Pretrained Transformer for Cooperative Aerial Mobility Control—0
ReZero: Enhancing LLM search ability by trying one-more-time—0
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
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Benchmark Results

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