A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
Error bounds of imitating policies and environments
Imitation learning trains a policy by mimicking expert demonstrations. Various imitation
methods were proposed and empirically evaluated, meanwhile, their theoretical …
methods were proposed and empirically evaluated, meanwhile, their theoretical …
Maximum-likelihood inverse reinforcement learning with finite-time guarantees
Inverse reinforcement learning (IRL) aims to recover the reward function and the associated
optimal policy that best fits observed sequences of states and actions implemented by an …
optimal policy that best fits observed sequences of states and actions implemented by an …
Self-directed online machine learning for topology optimization
Topology optimization by optimally distributing materials in a given domain requires non-
gradient optimizers to solve highly complicated problems. However, with hundreds of design …
gradient optimizers to solve highly complicated problems. However, with hundreds of design …
Learning from demonstration: Provably efficient adversarial policy imitation with linear function approximation
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an
expert demonstration so that its performance cannot be discriminated from the expert policy …
expert demonstration so that its performance cannot be discriminated from the expert policy …
Making reconstruction-based method great again for video anomaly detection
Anomaly detection in videos is a significant yet challenging problem. Previous approaches
based on deep neural networks employ either reconstruction-based or prediction-based …
based on deep neural networks employ either reconstruction-based or prediction-based …
Diffusion model-augmented behavioral cloning
Imitation learning addresses the challenge of learning by observing an expert's
demonstrations without access to reward signals from environments. Most existing imitation …
demonstrations without access to reward signals from environments. Most existing imitation …
Proximal point imitation learning
This work develops new algorithms with rigorous efficiency guarantees for infinite horizon
imitation learning (IL) with linear function approximation without restrictive coherence …
imitation learning (IL) with linear function approximation without restrictive coherence …
Error bounds of imitating policies and environments for reinforcement learning
In sequential decision-making, imitation learning (IL) trains a policy efficiently by mimicking
expert demonstrations. Various imitation methods were proposed and empirically evaluated …
expert demonstrations. Various imitation methods were proposed and empirically evaluated …
When demonstrations meet generative world models: A maximum likelihood framework for offline inverse reinforcement learning
Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards
and environment dynamics that underlie observed actions in a fixed, finite set of …
and environment dynamics that underlie observed actions in a fixed, finite set of …