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Imitation Learning

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Finally, a newer methodology, Inverse Q-Learning aims at directly learning Q-functions from expert data, implicitly representing rewards, under which the optimal policy can be given as a Boltzmann distribution similar to soft Q-learning

Source: Learning to Imitate

Papers

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Showing 1501–1525 of 2122 papers

TitleStatusHype
Learning with Language-Guided State Abstractions—0
Learning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning—0
Learning Your Way Without Map or Compass: Panoramic Target Driven Visual Navigation—0
Learn to Play Tetris with Deep Reinforcement Learning—0
Learn what matters: cross-domain imitation learning with task-relevant embeddings—0
LeTS-Drive: Driving in a Crowd by Learning from Tree Search—0
Leveraging Experience in Lazy Search—0
Leveraging Experience in Lazy Search—0
Leveraging Symmetries in Pick and Place—0
LHPF: Look back the History and Plan for the Future in Autonomous Driving—0
LLM-driven Imitation of Subrational Behavior : Illusion or Reality?—0
LLM-Personalize: Aligning LLM Planners with Human Preferences via Reinforced Self-Training for Housekeeping Robots—0
LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations—0
Local Bayesian Optimization of Motor Skills—0
Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion—0
Look Before You Leap: Using Serialized State Machine for Language Conditioned Robotic Manipulation—0
LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples—0
LORD: Large Models based Opposite Reward Design for Autonomous Driving—0
LOTUS: Continual Imitation Learning for Robot Manipulation Through Unsupervised Skill Discovery—0
LUMOS: Language-Conditioned Imitation Learning with World Models—0
M2Distill: Multi-Modal Distillation for Lifelong Imitation Learning—0
MAAIP: Multi-Agent Adversarial Interaction Priors for imitation from fighting demonstrations for physics-based characters—0
Make Bipedal Robots Learn How to Imitate—0
MANGA: Method Agnostic Neural-policy Generalization and Adaptation—0
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning—0
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