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

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Showing 8126–8150 of 15113 papers

TitleStatusHype
PAC Guarantees for Cooperative Multi-Agent Reinforcement Learning with Restricted Communication—0
Packet Routing with Graph Attention Multi-agent Reinforcement Learning—0
A Joint Planning and Learning Framework for Human-Aided Decision-Making—0
PAC Reinforcement Learning Algorithm for General-Sum Markov Games—0
PAC Reinforcement Learning for Predictive State Representations—0
PAC Reinforcement Learning with Rich Observations—0
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
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Benchmark Results

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