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 4301–4310 of 15113 papers

TitleStatusHype
Attention Graph for Multi-Robot Social Navigation with Deep Reinforcement Learning—0
A Reinforcement Learning Based Controller to Minimize Forces on the Crutches of a Lower-Limb Exoskeleton—0
A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC GuaranteesCode0
Safe Reinforcement Learning-Based Eco-Driving Control for Mixed Traffic Flows With Disturbances—0
Zero-Shot Reinforcement Learning via Function EncodersCode0
Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control—0
Augmenting Replay in World Models for Continual Reinforcement LearningCode0
Context-Former: Stitching via Latent Conditioned Sequence Modeling—0
The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function—0
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning—0
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Benchmark Results

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