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 4101–4110 of 15113 papers

TitleStatusHype
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment—0
Offline Imitation Learning from Multiple Baselines with Applications to Compiler Optimization—0
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies—0
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning—0
Towards Human-Centered Construction Robotics: A Reinforcement Learning-Driven Companion Robot for Contextually Assisting Carpentry Workers—0
Safe and Robust Reinforcement Learning: Principles and Practice—0
FPGA-Based Neural Thrust Controller for UAVs—0
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving—0
Image Deraining via Self-supervised Reinforcement Learning—0
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Benchmark Results

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