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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 1081–1090 of 2122 papers

TitleStatusHype
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming—0
Auto-Encoding Adversarial Imitation Learning—0
Imitation Learning for Generalizable Self-driving Policy with Sim-to-real TransferCode0
Latent Policies for Adversarial Imitation Learning—0
Imitate then Transcend: Multi-Agent Optimal Execution with Dual-Window Denoise PPO—0
Model-Based Imitation Learning Using Entropy Regularization of Model and Policy—0
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real worldCode2
Robust Imitation Learning against Variations in Environment DynamicsCode1
Learning Multi-Task Transferable Rewards via Variational Inverse Reinforcement Learning—0
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
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