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 10876–10900 of 15113 papers

TitleStatusHype
Randomized Policy Learning for Continuous State and Action MDPs—0
Tools for Data-driven Modeling of Within-Hand Manipulation with Underactuated Adaptive HandsCode0
Stable Reinforcement Learning with Unbounded State Space—0
Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems—0
Policy Optimization for H_2 Linear Control with H_ Robustness Guarantee: Implicit Regularization and Global Convergence—0
Maximum Entropy Model Rollouts: Fast Model Based Policy Optimization without Compounding Errors—0
Hallucinating Value: A Pitfall of Dyna-style Planning with Imperfect Environment Models—0
Learning to Plan via Deep Optimistic Value Exploration—0
Balancing a CartPole System with Reinforcement Learning -- A Tutorial—0
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning—0
A Comparison of Self-Play Algorithms Under a Generalized Framework—0
A Model-free Learning Algorithm for Infinite-horizon Average-reward MDPs with Near-optimal Regret—0
Constrained Upper Confidence Reinforcement Learning with Known Dynamics—0
Learning the model-free linear quadratic regulator via random search—0
Dual Policy DistillationCode0
Implications of Human Irrationality for Reinforcement Learning—0
Efficient Poverty Mapping using Deep Reinforcement Learning—0
Incorporating Pragmatic Reasoning Communication into Emergent Language—0
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping—0
Skill Discovery of Coordination in Multi-agent Reinforcement Learning—0
Real-Time Model Calibration with Deep Reinforcement Learning—0
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity—0
Stable and Efficient Policy Evaluation—0
Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning—0
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal AgentCode0
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Benchmark Results

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