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 7601–7650 of 15113 papers

TitleStatusHype
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming—0
Generating Paraphrases with Lean Vocabulary—0
Improving Factual Consistency Between a Response and Persona Facts—0
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture—0
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles—0
Generating stable molecules using imitation and reinforcement learning—0
Generating Student Feedback from Time-Series Data Using Reinforcement Learning—0
Generating Text with Deep Reinforcement Learning—0
Generation of Policy-Level Explanations for Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference—0
Generative Adversarial Self-Imitation Learning—0
Generative Adversarial Simulator—0
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs—0
Generative Exploration and Exploitation—0
Generative Inverse Deep Reinforcement Learning for Online Recommendation—0
Generative Job Recommendations with Large Language Model—0
Generative Memory for Lifelong Reinforcement Learning—0
Generative methods for sampling transition paths in molecular dynamics—0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Generative Slate Recommendation with Reinforcement Learning—0
Generator and Critic: A Deep Reinforcement Learning Approach for Slate Re-ranking in E-commerce—0
Genetic Algorithm enhanced by Deep Reinforcement Learning in parent selection mechanism and mutation : Minimizing makespan in permutation flow shop scheduling problems—0
Genetic Drift Regularization: on preventing Actor Injection from breaking Evolution Strategies—0
Genetic-Gated Networks for Deep Reinforcement—0
Genetic-Gated Networks for Deep Reinforcement Learning—0
Genetic Programming with Reinforcement Learning Trained Transformer for Real-World Dynamic Scheduling Problems—0
Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning—0
GenPO: Generative Diffusion Models Meet On-Policy Reinforcement Learning—0
GenTUS: Simulating User Behaviour and Language in Task-oriented Dialogues with Generative Transformers—0
Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction—0
Geometrically Coupled Monte Carlo Sampling—0
Geometric Entropic Exploration—0
Geometric Multi-Model Fitting by Deep Reinforcement Learning—0
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning—0
Getting By Goal Misgeneralization With a Little Help From a Mentor—0
GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks—0
GFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks—0
GitFL: Adaptive Asynchronous Federated Learning using Version Control—0
GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining—0
GITSR: Graph Interaction Transformer-based Scene Representation for Multi Vehicle Collaborative Decision-making—0
A Simulation Environment and Reinforcement Learning Method for Waste Reduction—0
G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning—0
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model—0
GLIDE-RL: Grounded Language Instruction through DEmonstration in RL—0
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning—0
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Benchmark Results

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