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 4191–4200 of 15113 papers

TitleStatusHype
Sample Efficient Myopic Exploration Through Multitask Reinforcement Learning with Diverse Tasks—0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey—0
Overestimation, Overfitting, and Plasticity in Actor-Critic: the Bitter Lesson of Reinforcement Learning—0
Conflict-Averse Gradient Aggregation for Constrained Multi-Objective Reinforcement Learning—0
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks—0
Robust Policy Learning via Offline Skill Diffusion—0
RL-GPT: Integrating Reinforcement Learning and Code-as-policy—0
Offline Fictitious Self-Play for Competitive Games—0
Investigating Gender Fairness in Machine Learning-driven Personalized Care for Chronic Pain—0
Reinforcement Learning and Graph Neural Networks for Probabilistic Risk Assessment—0
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Benchmark Results

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