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 4611–4620 of 15113 papers

TitleStatusHype
Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence—0
Analyzing Language Learned by an Active Question Answering Agent—0
Analyzing Policy Distillation on Multi-Task Learning and Meta-Reinforcement Learning in Meta-World—0
Analyzing the Hidden Activations of Deep Policy Networks: Why Representation Matters—0
Analyzing Visual Representations in Embodied Navigation Tasks—0
An Analysis of Model-Based Reinforcement Learning From Abstracted Observations—0
An Analysis of Categorical Distributional Reinforcement Learning—0
An Analysis of Deep Reinforcement Learning Agents for Text-based Games—0
An Analysis of Discretization Methods for Communication Learning with Multi-Agent Reinforcement Learning—0
An Analysis of Frame-skipping in Reinforcement Learning—0
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Benchmark Results

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