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 3351–3360 of 15113 papers

TitleStatusHype
The Curious Price of Distributional Robustness in Reinforcement Learning with a Generative Model—0
A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following AgentsCode0
Learning Interpretable Models of Aircraft Handling Behaviour by Reinforcement Learning from Human Feedback—0
Distributional Reinforcement Learning with Dual Expectile-Quantile Regression—0
Generating Synergistic Formulaic Alpha Collections via Reinforcement LearningCode3
Reward-Machine-Guided, Self-Paced Reinforcement LearningCode0
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion ModelsCode0
End-to-End Meta-Bayesian Optimisation with Transformer Neural ProcessesCode0
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement LearningCode1
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Benchmark Results

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