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 2821–2830 of 15113 papers

TitleStatusHype
Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning—0
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data CoverageCode0
Deep Reinforcement Learning for Weapons to Targets Assignment in a Hypersonic strike—0
Counterfactual-Augmented Importance Sampling for Semi-Offline Policy EvaluationCode0
Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning UpdatesCode0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
CROP: Conservative Reward for Model-based Offline Policy OptimizationCode1
Grow Your Limits: Continuous Improvement with Real-World RL for Robotic Locomotion—0
Demonstration-Regularized RL—0
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop—0
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Benchmark Results

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