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 1481–1490 of 15113 papers

TitleStatusHype
Masked Generative Priors Improve World Models Sequence Modelling Capabilities—0
Offline Inverse Constrained Reinforcement Learning for Safe-Critical Decision Making in Healthcare—0
Crafting desirable climate trajectories with RL explored socio-environmental simulationsCode0
Zero-Shot Generalization of Vision-Based RL Without Data Augmentation—0
Flipping-based Policy for Chance-Constrained Markov Decision Processes—0
Q-WSL: Optimizing Goal-Conditioned RL with Weighted Supervised Learning via Dynamic Programming—0
MotionRL: Align Text-to-Motion Generation to Human Preferences with Multi-Reward Reinforcement Learning—0
Retrieval-Augmented Decision Transformer: External Memory for In-context RLCode1
A Safety Modulator Actor-Critic Method in Model-Free Safe Reinforcement Learning and Application in UAV Hovering—0
On the Modeling Capabilities of Large Language Models for Sequential Decision Making—0
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Benchmark Results

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