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 2501–2510 of 15113 papers

TitleStatusHype
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
Zero-Shot Reinforcement Learning via Function EncodersCode0
Augmenting Replay in World Models for Continual Reinforcement LearningCode0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning—0
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function—0
LEACH-RLC: Enhancing IoT Data Transmission with Optimized Clustering and Reinforcement LearningCode0
Social Interpretable Reinforcement Learning—0
Health Text Simplification: An Annotated Corpus for Digestive Cancer Education and Novel Strategies for Reinforcement LearningCode0
On the Limitations of Markovian Rewards to Express Multi-Objective, Risk-Sensitive, and Modal Tasks—0
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Benchmark Results

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