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 3991–4000 of 15113 papers

TitleStatusHype
Out-of-Distribution Adaptation in Offline RL: Counterfactual Reasoning via Causal Normalizing Flows—0
Safe Reinforcement Learning with Learned Non-Markovian Safety Constraints—0
CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple CriticsCode0
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control—0
Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning—0
Zero-Sum Positional Differential Games as a Framework for Robust Reinforcement Learning: Deep Q-Learning Approach—0
A Model-based Multi-Agent Personalized Short-Video Recommender System—0
Learning Robust Autonomous Navigation and Locomotion for Wheeled-Legged Robots—0
Model-based reinforcement learning for protein backbone design—0
Proximal Curriculum with Task Correlations for Deep Reinforcement LearningCode0
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Benchmark Results

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