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 10376–10400 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning with Causal Structured World Models—0
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators—0
Offline Reinforcement Learning with Discrete Diffusion Skills—0
Offline Reinforcement Learning with Fisher Divergence Critic Regularization—0
Offline Reinforcement Learning with On-Policy Q-Function Regularization—0
Offline Reinforcement Learning with Resource Constrained Online Deployment—0
Offline Reinforcement Learning with Soft Behavior Regularization—0
Offline RL with Observation Histories: Analyzing and Improving Sample Complexity—0
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints—0
Offline Robotic World Model: Learning Robotic Policies without a Physics Simulator—0
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling—0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
Off-Policy Deep Reinforcement Learning Algorithms for Handling Various Robotic Manipulator Tasks—0
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift—0
Off-Policy Evaluation for Human Feedback—0
Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders—0
Off-Policy Evaluation in Partially Observable Environments—0
Off-Policy Evaluation via Off-Policy Classification—0
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory—0
Off-Policy Meta-Reinforcement Learning Based on Feature Embedding Spaces—0
Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift—0
Off-policy reinforcement learning for H_ control design—0
Off-Policy Reinforcement Learning with Delayed Rewards—0
Off-policy Reinforcement Learning with Optimistic Exploration and Distribution Correction—0
Off-Policy Reinforcement Learning with High Dimensional Reward—0
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Benchmark Results

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