methodology-6
Imitation Learning
3260 papers • 126 benchmarks • 313 datasets
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
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in imitation-learning-18
Trend
Dataset
Best Model
Actions
No benchmarks available.
Libraries
iUse these libraries to find imitation-learning-18 models and implementations
12 papers 386
Datasets
Subtasks
Most implemented papers
Generative Adversarial Imitation Learning
Stefano Ermon, Jonathan Ho•Thu Jun 09 2016
A new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning, is proposed and a certain instantiation of this framework draws an analogy between imitation learning and generative adversarial networks.
3509