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 1171–1180 of 15113 papers

TitleStatusHype
Autonomous Racing using a Hybrid Imitation-Reinforcement Learning ArchitectureCode1
Entropy-Regularized Process Reward ModelCode1
Behavior From the Void: Unsupervised Active Pre-TrainingCode1
Environment Agnostic Representation for Visual Reinforcement LearningCode1
Autonomous Reinforcement Learning: Formalism and BenchmarkingCode1
Efficient Reinforcement Learning Through Trajectory GenerationCode1
Efficient Recurrent Off-Policy RL Requires a Context-Encoder-Specific Learning RateCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Benchmarking Actor-Critic Deep Reinforcement Learning Algorithms for Robotics Control with Action ConstraintsCode1
Efficient Pressure: Improving efficiency for signalized intersectionsCode1
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Benchmark Results

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