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 5501–5525 of 15113 papers

TitleStatusHype
Robust Log-Optimal Strategy with Reinforcement Learning—0
Robust Longitudinal Control for Vehicular Autonomous Platoons Using Deep Reinforcement Learning—0
Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling—0
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control—0
Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamics—0
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning—0
Robust N-1 secure HV Grid Flexibility Estimation for TSO-DSO coordinated Congestion Management with Deep Reinforcement Learning—0
Robustness and risk management via distributional dynamic programming—0
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning—0
Robust Offline Reinforcement Learning -- Certify the Confidence Interval—0
Robust Offline Reinforcement Learning for Non-Markovian Decision Processes—0
Robust Offline Reinforcement Learning from Low-Quality Data—0
Robust Offline Reinforcement Learning with Gradient Penalty and Constraint Relaxation—0
Robust off-policy Reinforcement Learning via Soft Constrained Adversary—0
Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games—0
Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning—0
Robust Policy Learning over Multiple Uncertainty Sets—0
Robust Policy Learning via Offline Skill Diffusion—0
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments—0
Robust Predictable Control—0
Robust Preference Learning for Storytelling via Contrastive Reinforcement Learning—0
Robust Quadruped Jumping via Deep Reinforcement Learning—0
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning—0
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Benchmark Results

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