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 981–990 of 15113 papers

TitleStatusHype
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
End-to-End Affordance Learning for Robotic ManipulationCode1
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward EnvironmentsCode1
Training Efficient Controllers via Analytic Policy GradientCode1
Mastering the Unsupervised Reinforcement Learning Benchmark from PixelsCode1
Revisiting Discrete Soft Actor-CriticCode1
LCRL: Certified Policy Synthesis via Logically-Constrained Reinforcement LearningCode1
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Latent Plans for Task-Agnostic Offline Reinforcement LearningCode1
MAN: Multi-Action Networks LearningCode1
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Benchmark Results

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