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 7526–7550 of 15113 papers

TitleStatusHype
From "What" to "When" -- a Spiking Neural Network Predicting Rare Events and Time to their Occurrence—0
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems—0
Frugal Actor-Critic: Sample Efficient Off-Policy Deep Reinforcement Learning Using Unique Experiences—0
FSV: Learning to Factorize Soft Value Function for Cooperative Multi-Agent Reinforcement Learning—0
Full Gradient DQN Reinforcement Learning: A Provably Convergent Scheme—0
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning—0
Fully Convolutional Attention Networks for Fine-Grained Recognition—0
Fully Decentralized Model-based Policy Optimization with Networked Agents—0
Fully Decentralized Reinforcement Learning-based Control of Photovoltaics in Distribution Grids for Joint Provision of Real and Reactive Power—0
Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning—0
Functional Optimization Reinforcement Learning for Real-Time Bidding—0
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -—0
Fundamental Limits of Reinforcement Learning in Environment with Endogeneous and Exogeneous Uncertainty—0
Fuse and Adapt: Investigating the Use of Pre-Trained Self-Supervising Learning Models in Limited Data NLU problems—0
Fusion of Model-free Reinforcement Learning with Microgrid Control: Review and Vision—0
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer—0
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
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Benchmark Results

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