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 1621–1630 of 15113 papers

TitleStatusHype
Mobile Robot Path Planning in Dynamic Environments through Globally Guided Reinforcement LearningCode1
Mobility-Aware Cooperative Caching in Vehicular Edge Computing Based on Asynchronous Federated and Deep Reinforcement LearningCode1
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
A Versatile and Efficient Reinforcement Learning Framework for Autonomous DrivingCode1
Model-based Adversarial Meta-Reinforcement LearningCode1
Model-based graph reinforcement learning for inductive traffic signal controlCode1
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise RolloutsCode1
Model-based Policy Optimization with Unsupervised Model AdaptationCode1
Model-Based Reinforcement Learning via Latent-Space CollocationCode1
Coach-Player Multi-Agent Reinforcement Learning for Dynamic Team CompositionCode1
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Benchmark Results

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