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 11026–11050 of 15113 papers

TitleStatusHype
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—0
FuzzerGym: A Competitive Framework for Fuzzing and Learning—0
FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer—0
Fuzzy Controller of Reward of Reinforcement Learning For Handwritten Digit Recognition—0
FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL—0
GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis—0
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations—0
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
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Benchmark Results

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