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 3611–3620 of 15113 papers

TitleStatusHype
Unsupervised-to-Online Reinforcement Learning—0
MiWaves Reinforcement Learning AlgorithmCode0
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
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
Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Multi-Agent Target Assignment and Path Finding for Intelligent Warehouse: A Cooperative Multi-Agent Deep Reinforcement Learning Perspective—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
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Benchmark Results

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