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 941–950 of 15113 papers

TitleStatusHype
Active Exploration for Inverse Reinforcement LearningCode1
GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systemsCode1
Reliable Conditioning of Behavioral Cloning for Offline Reinforcement LearningCode1
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Constrained episodic reinforcement learning in concave-convex and knapsack settingsCode1
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
Continuous Deep Q-Learning with Model-based AccelerationCode1
Deep Active Inference for Partially Observable MDPsCode1
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Benchmark Results

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