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 121–130 of 15113 papers

TitleStatusHype
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online VideosCode3
CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning AlgorithmsCode3
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement LearningCode3
Tianshou: a Highly Modularized Deep Reinforcement Learning LibraryCode3
FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative FinanceCode3
Fine-Tuning Language Models from Human PreferencesCode3
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
Dopamine: A Research Framework for Deep Reinforcement LearningCode3
Practical Deep Reinforcement Learning Approach for Stock TradingCode3
Deep Reinforcement LearningCode3
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Benchmark Results

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