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 3226–3250 of 15113 papers

TitleStatusHype
Data-Driven LQR using Reinforcement Learning and Quadratic Neural Networks—0
Data-driven modeling and supervisory control system optimization for plug-in hybrid electric vehicles—0
Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey—0
Data-Driven Offline Decision-Making via Invariant Representation Learning—0
Data-driven Outer-Loop Control Using Deep Reinforcement Learning for Trajectory Tracking—0
Credit Assignment Techniques in Stochastic Computation Graphs—0
Data Driven Reward Initialization for Preference based Reinforcement Learning—0
Data-Driven Robust Control Using Reinforcement Learning—0
Data-Driven Simulation of Ride-Hailing Services using Imitation and Reinforcement Learning—0
A Survey of Constraint Formulations in Safe Reinforcement Learning—0
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
A survey of benchmarking frameworks for reinforcement learning—0
Creativity of AI: Hierarchical Planning Model Learning for Facilitating Deep Reinforcement Learning—0
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control—0
Data-Efficient Exploration with Self Play for Atari—0
Asynchronous training of quantum reinforcement learning—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
Data-Efficient Learning of Feedback Policies from Image Pixels using Deep Dynamical Models—0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks—0
Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics—0
Data Efficient Reinforcement Learning for Legged Robots—0
Creativity in Robot Manipulation with Deep Reinforcement Learning—0
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Benchmark Results

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