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 10501–10525 of 15113 papers

TitleStatusHype
Policy Optimization for H_2 Linear Control with H_ Robustness Guarantee: Implicit Regularization and Global Convergence—0
Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive HandsCode0
Online Data Poisoning Attacks—0
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems—0
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors—0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
Conservative Q-Learning for Offline Reinforcement LearningCode1
A Comparison of Self-Play Algorithms Under a Generalized Framework—0
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning—0
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
Learning to Play No-Press Diplomacy with Best Response Policy IterationCode1
Stable Reinforcement Learning with Unbounded State Space—0
Reinforcement Learning Under Moral UncertaintyCode1
Randomized Policy Learning for Continuous State and Action MDPs—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Reinforcement Learning for Multi-Product Multi-Node Inventory Management in Supply ChainsCode1
Real-Time Model Calibration with Deep Reinforcement Learning—0
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping—0
Efficient Poverty Mapping using Deep Reinforcement Learning—0
Dual Policy DistillationCode0
Implications of Human Irrationality for Reinforcement Learning—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Randomized Entity-wise Factorization for Multi-Agent Reinforcement LearningCode1
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
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Benchmark Results

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