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 2701–2710 of 15113 papers

TitleStatusHype
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Not All Rollouts are Useful: Down-Sampling Rollouts in LLM Reinforcement Learning—0
Prejudge-Before-Think: Enhancing Large Language Models at Test-Time by Process Prejudge ReasoningCode0
TraCeS: Trajectory Based Credit Assignment From Sparse Safety Feedback—0
RL-PINNs: Reinforcement Learning-Driven Adaptive Sampling for Efficient Training of PINNs—0
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard—0
Crossing the Human-Robot Embodiment Gap with Sim-to-Real RL using One Human Demonstration—0
Evolutionary Policy Optimization—0
Control of Rayleigh-Bénard Convection: Effectiveness of Reinforcement Learning in the Turbulent RegimeCode0
VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning—0
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Benchmark Results

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