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 7351–7375 of 15113 papers

TitleStatusHype
L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning—0
Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle—0
Learning to Mitigate AI Collusion on Economic Platforms—0
Energy-Efficient Parking Analytics System using Deep Reinforcement LearningCode0
Learning Reward Models for Cooperative Trajectory Planning with Inverse Reinforcement Learning and Monte Carlo Tree SearchCode0
Convex Programs and Lyapunov Functions for Reinforcement Learning: A Unified Perspective on the Analysis of Value-Based Methods—0
Statistical Inference After Adaptive Sampling for Longitudinal Data—0
Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalization—0
Robust Policy Learning over Multiple Uncertainty Sets—0
Reinforcement Learning in Presence of Discrete Markovian Context Evolution—0
Sequential Bayesian experimental designs via reinforcement learning—0
Saute RL: Almost Surely Safe Reinforcement Learning Using State Augmentation—0
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality—0
Motivating Physical Activity via Competitive Human-Robot Interaction—0
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost—0
Deep Reinforcement Learning and Convex Mean-Variance Optimisation for Portfolio Management—0
Individual-Level Inverse Reinforcement Learning for Mean Field Games—0
Goal Recognition as Reinforcement LearningCode0
Autonomous Drone Swarm Navigation and Multi-target Tracking in 3D Environments with Dynamic Obstacles—0
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation—0
Neural NID Rules—0
Robust Learning from Observation with Model MisspecificationCode0
Rate-matching the regret lower-bound in the linear quadratic regulator with unknown dynamics—0
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
Regularized Q-learning—0
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Benchmark Results

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