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 2051–2060 of 15113 papers

TitleStatusHype
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
TrojanForge: Generating Adversarial Hardware Trojan Examples Using Reinforcement Learning—0
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree SearchCode1
Cross-Domain Policy Adaptation by Capturing Representation MismatchCode1
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks—0
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality—0
PEAC: Unsupervised Pre-training for Cross-Embodiment Reinforcement LearningCode1
Offline Reinforcement Learning from Datasets with Structured Non-StationarityCode0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
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Benchmark Results

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