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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7551–7575 of 15113 papers

TitleStatusHype
GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing—0
GAN-Based Interactive Reinforcement Learning from Demonstration and Human Evaluative Feedback—0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Gap-Increasing Policy Evaluation for Efficient and Noise-Tolerant Reinforcement Learning—0
Gaussian-Mixture-Model Q-Functions for Reinforcement Learning by Riemannian Optimization—0
Gaussian Process Policy Optimization—0
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning—0
GDI: Rethinking What Makes Reinforcement Learning Different From Supervised Learning—0
GDI: Rethinking What Makes Reinforcement Learning Different from Supervised Learning—0
Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs—0
Uncertainty Estimation Using Riemannian Model~Dynamics for Offline Reinforcement Learning—0
GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning—0
General Intelligence Requires Rethinking Exploration—0
Generalisation in Lifelong Reinforcement Learning through Logical Composition—0
Generalised Policy Improvement with Geometric Policy Composition—0
Efficient Reinforcement Learning by Guiding Generalist World Models with Non-Curated Data—0
Generalization in Deep RL for TSP Problems via Equivariance and Local Search—0
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning—0
Generalization in Generation: A closer look at Exposure Bias—0
Generalization in Monitored Markov Decision Processes (Mon-MDPs)—0
Generalization in Transfer Learning—0
Generalization of Compositional Tasks with Logical Specification via Implicit Planning—0
Generalization of Deep Reinforcement Learning for Jammer-Resilient Frequency and Power Allocation—0
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation—0
Generalization Through the Lens of Learning Dynamics—0
Show:102550
← PrevPage 303 of 605Next →

Benchmark Results

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