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 3941–3950 of 15113 papers

TitleStatusHype
Exclusively Penalized Q-learning for Offline Reinforcement Learning—0
A finite time analysis of distributed Q-learning—0
Policy Gradient Methods for Risk-Sensitive Distributional Reinforcement Learning with Provable Convergence—0
Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks—0
Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings—0
Autonomous Algorithm for Training Autonomous Vehicles with Minimal Human Intervention—0
Learning to sample fibers for goodness-of-fit testing—0
Lusifer: LLM-based User SImulated Feedback Environment for online Recommender systemsCode0
Leader Reward for POMO-Based Neural Combinatorial Optimization—0
HighwayLLM: Decision-Making and Navigation in Highway Driving with RL-Informed Language Model—0
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Benchmark Results

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