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 6601–6625 of 15113 papers

TitleStatusHype
Do Artificial Reinforcement-Learning Agents Matter Morally?—0
Do as I can, not as I get—0
Do Autonomous Agents Benefit from Hearing?—0
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances—0
Document-editing Assistants and Model-based Reinforcement Learning as a Path to Conversational AI—0
Do Deep Reinforcement Learning Algorithms really Learn to Navigate?—0
Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?—0
How Does an Approximate Model Help in Reinforcement Learning?—0
Does Sparsity Help in Learning Misspecified Linear Bandits?—0
Domain Adaptation for Deep Reinforcement Learning in Visually Distinct Games—0
Domain Adaptation for Offline Reinforcement Learning with Limited Samples—0
Domain Adaptation for Reinforcement Learning on the Atari—0
Domain Adaptation of Reinforcement Learning Agents based on Network Service Proximity—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Domain Adapting Deep Reinforcement Learning for Real-world Speech Emotion Recognition—0
Domain Adaptive Fake News Detection via Reinforcement Learning—0
Domain Adversarial Reinforcement Learning—0
Domain Adversarial Reinforcement Learning for Partial Domain Adaptation—0
Domain Generalization for Robust Model-Based Offline Reinforcement Learning—0
Domain-Independent Optimistic Initialization for Reinforcement Learning—0
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning—0
Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning—0
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning—0
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots—0
Domain Randomization via Entropy Maximization—0
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Benchmark Results

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