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 8301–8325 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning Hands-On—0
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: The Power of Gaps—0
Offline Reinforcement Learning with Pseudometric Learning—0
Offline reinforcement learning with uncertainty for treatment strategies in sepsis—0
Offline Reinforcement Learning with Realizability and Single-policy Concentrability—0
Offline Reinforcement Learning with Differential Privacy—0
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes—0
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient—0
Offline Reinforcement Learning with Imbalanced Datasets—0
Offline Reinforcement Learning with Behavioral Supervisor Tuning—0
Offline Reinforcement Learning with Adaptive Behavior Regularization—0
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
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Benchmark Results

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