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 1881–1890 of 15113 papers

TitleStatusHype
DISK: Learning local features with policy gradientCode1
Experience Replay with Likelihood-free Importance WeightsCode1
Automatic Data Augmentation for Generalization in Deep Reinforcement LearningCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
Expert-Supervised Reinforcement Learning for Offline Policy Learning and EvaluationCode1
Learning with AMIGo: Adversarially Motivated Intrinsic GoalsCode1
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic EnvironmentsCode1
Safe Reinforcement Learning via Curriculum InductionCode1
Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement LearningCode1
Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement LearningCode1
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Benchmark Results

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