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 4461–4470 of 15113 papers

TitleStatusHype
A Hierarchical Hybrid Learning Framework for Multi-agent Trajectory Prediction—0
A Hierarchical Model for Device Placement—0
A Hierarchical Reinforcement Learning Method for Persistent Time-Sensitive Tasks—0
A Hierarchical Two-tier Approach to Hyper-parameter Optimization in Reinforcement Learning—0
A Homogenization Approach for Gradient-Dominated Stochastic Optimization—0
A Human-Centered Data-Driven Planner-Actor-Critic Architecture via Logic Programming—0
A Human-Centered Safe Robot Reinforcement Learning Framework with Interactive Behaviors—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
A Hybrid Approach Between Adversarial Generative Networks and Actor-Critic Policy Gradient for Low Rate High-Resolution Image Compression—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
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Benchmark Results

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