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 3771–3780 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for mmWave Initial Beam Alignment—0
Learning to Forecast Aleatoric and Epistemic Uncertainties over Long Horizon Trajectories—0
Data Driven Reward Initialization for Preference based Reinforcement Learning—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Robot path planning using deep reinforcement learning—0
Exploiting Unlabeled Data for Feedback Efficient Human Preference based Reinforcement Learning—0
Post Reinforcement Learning InferenceCode0
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning—0
Dual RL: Unification and New Methods for Reinforcement and Imitation LearningCode1
Tuning computer vision models with task rewards—0
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Benchmark Results

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