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 2351–2360 of 15113 papers

TitleStatusHype
Twisting Lids Off with Two Hands—0
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Improving the Validity of Automatically Generated Feedback via Reinforcement LearningCode1
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning—0
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning—0
Robust Policy Learning via Offline Skill Diffusion—0
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks—0
Large Language Models are Learnable Planners for Long-Term RecommendationCode1
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Benchmark Results

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