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 1651–1660 of 15113 papers

TitleStatusHype
RAIN: Reinforcement Algorithms for Improving Numerical Weather and Climate ModelsCode0
Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion—0
Atari-GPT: Benchmarking Multimodal Large Language Models as Low-Level Policies in Atari Games—0
Evaluating the Impact of Multiple DER Aggregators on Wholesale Energy Markets: A Hybrid Mean Field Approach—0
Optimization Solution Functions as Deterministic Policies for Offline Reinforcement Learning—0
Unsupervised-to-Online Reinforcement Learning—0
Learning Robust Reward Machines from Noisy LabelsCode0
What makes math problems hard for reinforcement learning: a case studyCode1
Simultaneous Training of First- and Second-Order Optimizers in Population-Based Reinforcement Learning—0
Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper—0
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Benchmark Results

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