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 3201–3225 of 15113 papers

TitleStatusHype
Credit-cognisant reinforcement learning for multi-agent cooperation—0
Asymmetric REINFORCE for off-Policy Reinforcement Learning: Balancing positive and negative rewards—0
Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning—0
Credit Assignment with Meta-Policy Gradient for Multi-Agent Reinforcement Learning—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
Data-assimilated model-informed reinforcement learning—0
Asymptotic Instance-Optimal Algorithms for Interactive Decision Making—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Data Center Cooling System Optimization Using Offline Reinforcement Learning—0
Asymptotics of Reinforcement Learning with Neural Networks—0
Data Cross-Segmentation for Improved Generalization in Reinforcement Learning Based Algorithmic Trading—0
Data Distillation for Controlling Specificity in Dialogue Generation—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
A Survey of Continual Reinforcement Learning—0
Data-driven control of COVID-19 in buildings: a reinforcement-learning approach—0
Data-driven control of micro-climate in buildings: an event-triggered reinforcement learning approach—0
Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning—0
Data-driven Dynamic Multi-objective Optimal Control: An Aspiration-satisfying Reinforcement Learning Approach—0
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces—0
Data-Driven Evaluation of Training Action Space for Reinforcement Learning—0
Data-Driven H-infinity Control with a Real-Time and Efficient Reinforcement Learning Algorithm: An Application to Autonomous Mobility-on-Demand Systems—0
Data-driven Integrated Sensing and Communication: Recent Advances, Challenges, and Future Prospects—0
Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games—0
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps—0
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Benchmark Results

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