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 751–760 of 15113 papers

TitleStatusHype
Adversarially Trained Actor Critic for Offline Reinforcement LearningCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Does Zero-Shot Reinforcement Learning Exist?Code1
Benchmarking Reinforcement Learning Techniques for Autonomous NavigationCode1
Don't Change the Algorithm, Change the Data: Exploratory Data for Offline Reinforcement LearningCode1
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative ExplorationCode1
Tactical Optimism and Pessimism for Deep Reinforcement LearningCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
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Benchmark Results

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