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 2841–2850 of 15113 papers

TitleStatusHype
Corruption-Robust Offline Reinforcement Learning with General Function ApproximationCode0
Enhancing Robotic Manipulation: Harnessing the Power of Multi-Task Reinforcement Learning and Single Life Reinforcement Learning in Meta-World—0
Safe Navigation: Training Autonomous Vehicles using Deep Reinforcement Learning in CARLACode1
A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare—0
Diversify Question Generation with Retrieval-Augmented Style TransferCode1
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation—0
Diverse Priors for Deep Reinforcement Learning—0
Iteratively Learn Diverse Strategies with State Distance Information—0
Provable Benefits of Multi-task RL under Non-Markovian Decision Making Processes—0
Automatic Unit Test Data Generation and Actor-Critic Reinforcement Learning for Code SynthesisCode1
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Benchmark Results

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