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 8151–8175 of 15113 papers

TitleStatusHype
Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors—0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Paraphrase Generation with Deep Reinforcement Learning—0
Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation—0
Parenting: Safe Reinforcement Learning from Human Input—0
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks—0
Pareto Frontier Approximation Network (PA-Net) to Solve Bi-objective TSP—0
Pareto Policy Adaptation—0
Pareto Policy Pool for Model-based Offline Reinforcement Learning—0
Pareto Set Learning for Multi-Objective Reinforcement Learning—0
ParMod: A Parallel and Modular Framework for Learning Non-Markovian Tasks—0
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning—0
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
Part-Activated Deep Reinforcement Learning for Action Prediction—0
Partial End-to-end Reinforcement Learning for Robustness Against Modelling Error in Autonomous Racing—0
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach—0
Partially Detected Intelligent Traffic Signal Control: Environmental Adaptation—0
Partially Observable Multi-Agent Reinforcement Learning with Information Sharing—0
Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms—0
Partial Off-Policy Learning: Balance Accuracy and Diversity for Human-Oriented Image Captioning—0
Partial Policy-based Reinforcement Learning for Anatomical Landmark Localization in 3D Medical Images—0
Partial Simulation for Imitation Learning—0
Particle Based Stochastic Policy Optimization—0
Show:102550
← PrevPage 327 of 605Next →

Benchmark Results

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