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 1431–1440 of 15113 papers

TitleStatusHype
ORSO: Accelerating Reward Design via Online Reward Selection and Policy OptimizationCode0
Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving—0
Reinforcement Learning with Euclidean Data Augmentation for State-Based Continuous ControlCode0
Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide ExplorationCode0
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving—0
Reinforcement Learning with LTL and ω-Regular Objectives via Optimality-Preserving Translation to Average Rewards—0
Neural-based Control for CubeSat Docking Maneuvers—0
Dynamic Learning Rate for Deep Reinforcement Learning: A Bandit Approach—0
When to Trust Your Data: Enhancing Dyna-Style Model-Based Reinforcement Learning With Data Filter—0
EdgeRL: Reinforcement Learning-driven Deep Learning Model Inference Optimization at Edge—0
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Benchmark Results

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