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 11301–11350 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
UAV Aided Search and Rescue Operation Using Reinforcement Learning—0
Value-driven Hindsight Modelling—0
Optimistic Policy Optimization with Bandit Feedback—0
Keep Doing What Worked: Behavioral Modelling Priors for Offline Reinforcement Learning—0
Efficient Deep Reinforcement Learning via Adaptive Policy TransferCode0
Curriculum in Gradient-Based Meta-Reinforcement Learning—0
KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge—0
Empirical Policy Evaluation with Supergraphs—0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Multi-Issue Bargaining With Deep Reinforcement Learning—0
MoTiAC: Multi-Objective Actor-Critics for Real-Time Bidding—0
Reinforcement learning for the privacy preservation and manipulation of eye tracking data—0
Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning—0
Langevin DQNCode0
Control Frequency Adaptation via Action Persistence in Batch Reinforcement LearningCode0
Adaptive Experience Selection for Policy Gradient—0
Investigating Simple Object Representations in Model-Free Deep Reinforcement Learning—0
Non-asymptotic Convergence of Adam-type Reinforcement Learning Algorithms under Markovian Sampling—0
The Archimedean trap: Why traditional reinforcement learning will probably not yield AGI—0
Universal Value Density Estimation for Imitation Learning and Goal-Conditioned Reinforcement LearningCode0
Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning—0
Robust Reinforcement Learning via Adversarial training with Langevin DynamicsCode0
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement LearningCode0
Deep Reinforcement Learning-Based Beam Tracking for Low-Latency Services in Vehicular Networks—0
Improving Generalization of Reinforcement Learning with Minimax Distributional Soft Actor-Critic—0
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Benchmark Results

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