Imitation Learning
653 papers with code • 0 benchmarks • 20 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
Benchmarks
These leaderboards are used to track progress in Imitation Learning
Libraries
Use these libraries to find Imitation Learning models and implementationsDatasets
CARLA
AirSim
AI2-THOR
RLBench
TORCS
INTERACTION Dataset
ManiSkill2
CoDraw
CubiCasa5K
CHALET
Most implemented papers
Generative Adversarial Imitation Learning
hill-a/stable-baselines
•
•
NeurIPS 2016
Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal.
End-to-end Driving via Conditional Imitation Learning
carla-simulator/imitation-learning
•
•
However, driving policies trained via imitation learning cannot be controlled at test time.
Behavioral Cloning from Observation
In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects.
Exact Combinatorial Optimization with Graph Convolutional Neural Networks
ds4dm/learn2branch
•
•
NeurIPS 2019
Combinatorial optimization problems are typically tackled by the branch-and-bound paradigm.
Deep Q-learning from Demonstrations
We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism.
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
reinforcement-learning-kr/lets-do-irl
•
•
ICLR 2019
By enforcing a constraint on the mutual information between the observations and the discriminator's internal representation, we can effectively modulate the discriminator's accuracy and maintain useful and informative gradients.
SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
opendilab/DI-engine
•
•
ICLR 2020
Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.
IQ-Learn: Inverse soft-Q Learning for Imitation
Div99/IQ-Learn
•
•
NeurIPS 2021
In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
ermongroup/InfoGAIL
•
•
NeurIPS 2017
The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal.
Self-Imitation Learning
junhyukoh/self-imitation-learning
•
•
ICML 2018
This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions.
