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 11101–11125 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 445 of 605Next →

Benchmark Results

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