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 931–940 of 15113 papers

TitleStatusHype
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement LearningCode1
Rethinking Value Function Learning for Generalization in Reinforcement LearningCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain AdaptationCode1
Teacher Forcing Recovers Reward Functions for Text GenerationCode1
On Uncertainty in Deep State Space Models for Model-Based Reinforcement LearningCode1
When to Update Your Model: Constrained Model-based Reinforcement LearningCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization AlgorithmCode1
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
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Benchmark Results

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