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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8326–8350 of 15113 papers

TitleStatusHype
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
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error—0
Off-Policy Risk-Sensitive Reinforcement Learning Based Constrained Robust Optimal Control—0
Off-Policy Selection for Initiating Human-Centric Experimental Design—0
Off-Policy Self-Critical Training for Transformer in Visual Paragraph Generation—0
Off-Policy Shaping Ensembles in Reinforcement Learning—0
OffRIPP: Offline RL-based Informative Path Planning—0
Off-road Autonomous Vehicles Traversability Analysis and Trajectory Planning Based on Deep Inverse Reinforcement Learning—0
Offsetting Unequal Competition through RL-assisted Incentive Schemes—0
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research—0
Of Mice and Machines: A Comparison of Learning Between Real World Mice and RL Agents—0
OIL: Observational Imitation Learning—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
O-MAPL: Offline Multi-agent Preference Learning—0
Omega-Regular Objectives in Model-Free Reinforcement Learning—0
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Benchmark Results

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