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 2251–2260 of 15113 papers

TitleStatusHype
Reinforcement Learning in Agent-Based Market Simulation: Unveiling Realistic Stylized Facts and Behavior—0
Jointly Training and Pruning CNNs via Learnable Agent Guidance and Alignment—0
Robustness and Visual Explanation for Black Box Image, Video, and ECG Signal Classification with Reinforcement Learning—0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
FPGA-Based Neural Thrust Controller for UAVs—0
CaT: Constraints as Terminations for Legged Locomotion Reinforcement Learning—0
Long and Short-Term Constraints Driven Safe Reinforcement Learning for Autonomous Driving—0
Probabilistic Model Checking of Stochastic Reinforcement Learning Policies—0
Image Deraining via Self-supervised Reinforcement Learning—0
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no LibrariesCode0
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Benchmark Results

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