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 581–590 of 15113 papers

TitleStatusHype
ToolRL: Reward is All Tool Learning NeedsCode0
VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
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
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent RegimeCode0
ReTool: Reinforcement Learning for Strategic Tool Use in LLMs—0
Position Paper: Rethinking Privacy in RL for Sequential Decision-making in the Age of LLMs—0
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling—0
Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution gridsCode0
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Benchmark Results

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