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 4121–4130 of 15113 papers

TitleStatusHype
Bandit approach to conflict-free multi-agent Q-learning in view of photonic implementation—0
Inverse Reinforcement Learning for Text Summarization—0
Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance—0
Taming Lagrangian Chaos with Multi-Objective Reinforcement Learning—0
Quantum policy gradient algorithms—0
Near-optimal Policy Identification in Active Reinforcement Learning—0
Neural Coreference Resolution based on Reinforcement Learning—0
Risk-Sensitive Reinforcement Learning with Exponential Criteria—0
Managing Temporal Resolution in Continuous Value Estimation: A Fundamental Trade-off—0
Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning—0
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Benchmark Results

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