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 11301–11325 of 15113 papers

TitleStatusHype
Reward Shaping for Human Learning via Inverse Reinforcement LearningCode0
Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity—0
Scalable Multi-Agent Inverse Reinforcement Learning via Actor-Attention-Critic—0
Millimeter Wave Communications with an Intelligent Reflector: Performance Optimization and Distributional Reinforcement Learning—0
Safe reinforcement learning for probabilistic reachability and safety specifications: A Lyapunov-based approachCode0
Optimizing Traffic Lights with Multi-agent Deep Reinforcement Learning and V2X communication—0
Wireless 2.0: Towards an Intelligent Radio Environment Empowered by Reconfigurable Meta-Surfaces and Artificial Intelligence—0
Near-optimal Regret Bounds for Stochastic Shortest Path—0
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data—0
Deep Reinforcement Learning with Linear Quadratic Regulator Regions—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion—0
Adversarial Radar Inference. From Inverse Tracking to Inverse Reinforcement Learning of Cognitive Radar—0
Vehicle Tracking in Wireless Sensor Networks via Deep Reinforcement Learning—0
On the Search for Feedback in Reinforcement Learning—0
Accelerating Reinforcement Learning with a Directional-Gaussian-Smoothing Evolution Strategy—0
Data Freshness and Energy-Efficient UAV Navigation Optimization: A Deep Reinforcement Learning Approach—0
Disentangling Controllable Object through Video Prediction Improves Visual Reinforcement Learning—0
Adaptive Temporal Difference Learning with Linear Function Approximation—0
Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search—0
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning—0
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Future Challenges—0
oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions—0
Debiased Off-Policy Evaluation for Recommendation Systems—0
Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems—0
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Benchmark Results

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