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 3661–3670 of 15113 papers

TitleStatusHype
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs—0
Integrating Controllable Motion Skills from Demonstrations—0
Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise—0
Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic LearningCode0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation—0
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Benchmark Results

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