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 8751–8775 of 15113 papers

TitleStatusHype
PAG: Multi-Turn Reinforced LLM Self-Correction with Policy as Generative Verifier—0
PaintBot: A Reinforcement Learning Approach for Natural Media Painting—0
Pairwise heuristic sequence alignment algorithm based on deep reinforcement learning—0
Adaptive Pairwise Weights for Temporal Credit Assignment—0
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining—0
Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning—0
Pangu DeepDiver: Adaptive Search Intensity Scaling via Open-Web Reinforcement Learning—0
PEaRL: Personalized Privacy of Human-Centric Systems using Early-Exit Reinforcement Learning—0
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations—0
Parallel Automatic History Matching Algorithm Using Reinforcement Learning—0
Parallel bandit architecture based on laser chaos for reinforcement learning—0
Parallelized Reverse Curriculum Generation—0
Parallel Knowledge Transfer in Multi-Agent Reinforcement Learning—0
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation—0
Parameter-free Gradient Temporal Difference Learning—0
Parameterized MDPs and Reinforcement Learning Problems -- A Maximum Entropy Principle Based Framework—0
Parameterized Reinforcement Learning for Optical System Optimization—0
Parameter Optimization of LLC-Converter with multiple operation points using Reinforcement Learning—0
Parameter Sharing Deep Deterministic Policy Gradient for Cooperative Multi-agent Reinforcement Learning—0
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
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Benchmark Results

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