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 5576–5600 of 15113 papers

TitleStatusHype
Safe Control and Learning Using the Generalized Action Governor—0
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning—0
Debiased Off-Policy Evaluation for Recommendation Systems—0
Safe Coupled Deep Q-Learning for Recommendation Systems—0
Safety-Critical Learning of Robot Control with Temporal Logic Specifications—0
Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning—0
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks—0
Safe Deep Reinforcement Learning by Verifying Task-Level Properties—0
Safe Distributional Reinforcement Learning—0
Safe Domain Randomization via Uncertainty-Aware Out-of-Distribution Detection and Policy Adaptation—0
Safe Evaluation For Offline Learning: Are We Ready To Deploy?—0
Safe Exploration by Solving Early Terminated MDP—0
Safe Exploration for Identifying Linear Systems via Robust Optimization—0
Safe Exploration in Linear Equality Constraint—0
Safe Exploration in Model-based Reinforcement Learning using Control Barrier Functions—0
Safe Exploration in Reinforcement Learning: Training Backup Control Barrier Functions with Zero Training Time Safety Violations—0
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms—0
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
Safe Exploration of State and Action Spaces in Reinforcement Learning—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Safe Inverse Reinforcement Learning via Control Barrier Function—0
Safe Learning and Optimization Techniques: Towards a Survey of the State of the Art—0
Safe Model-based Off-policy Reinforcement Learning for Eco-Driving in Connected and Automated Hybrid Electric Vehicles—0
Safe and Efficient Reinforcement Learning Using Disturbance-Observer-Based Control Barrier Functions—0
Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units—0
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Benchmark Results

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