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 1371–1380 of 15113 papers

TitleStatusHype
Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning—0
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure—0
ODRL: A Benchmark for Off-Dynamics Reinforcement LearningCode2
LongReward: Improving Long-context Large Language Models with AI FeedbackCode2
Getting By Goal Misgeneralization With a Little Help From a Mentor—0
FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning AgentsCode0
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning—0
GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks—0
Off-Policy Selection for Initiating Human-Centric Experimental Design—0
OGBench: Benchmarking Offline Goal-Conditioned RLCode3
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Benchmark Results

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