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 1871–1880 of 15113 papers

TitleStatusHype
Evaluating the Performance of Reinforcement Learning AlgorithmsCode1
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEsCode1
Image Classification by Reinforcement Learning with Two-State Q-LearningCode1
A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity RewardsCode1
What can I do here? A Theory of Affordances in Reinforcement LearningCode1
Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data AugmentationCode1
Critic Regularized RegressionCode1
Intrinsic Reward Driven Imitation Learning via Generative ModelCode1
Online 3D Bin Packing with Constrained Deep Reinforcement LearningCode1
The NetHack Learning EnvironmentCode1
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Benchmark Results

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