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 91–100 of 15113 papers

TitleStatusHype
RL-Guided MPC for Autonomous Greenhouse Control—0
The Courage to Stop: Overcoming Sunk Cost Fallacy in Deep Reinforcement Learning—0
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement LearningCode2
Value-Free Policy Optimization via Reward PartitioningCode0
Ego-R1: Chain-of-Tool-Thought for Ultra-Long Egocentric Video Reasoning—0
A Production Scheduling Framework for Reinforcement Learning Under Real-World ConstraintsCode1
StaQ it! Growing neural networks for Policy Mirror Descent—0
ReinDSplit: Reinforced Dynamic Split Learning for Pest Recognition in Precision Agriculture—0
Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes—0
Overcoming Overfitting in Reinforcement Learning via Gaussian Process Diffusion PolicyCode0
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Benchmark Results

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