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Imitation Learning

131 papers with code · Methodology

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.

Source: Adversarial Imitation Learning from Incomplete Demonstrations

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Latest papers with code

Imitation Learning with Sinkhorn Distances

20 Aug 2020gpapagiannis/sinkhorn-imitation

In this paper, we present tractable solutions by formulating imitation learning as minimization of the Sinkhorn distance between occupancy measures.

IMITATION LEARNING

3
20 Aug 2020

Imitating Unknown Policies via Exploration

13 Aug 2020NathanGavenski/IUPE

Behavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations.

IMITATION LEARNING

8
13 Aug 2020

Non-Adversarial Imitation Learning and its Connections to Adversarial Methods

8 Aug 2020OlegArenz/O-NAIL

We also show that our non-adversarial formulation can be used to derive novel algorithms by presenting a method for offline imitation learning that is inspired by the recent ValueDice algorithm, but does not rely on small policy updates for convergence.

IMITATION LEARNING

0
08 Aug 2020

Generalization Guarantees for Multi-Modal Imitation Learning

5 Aug 2020irom-lab/PAC-Imitation

Control policies from imitation learning can often fail to generalize to novel environments due to imperfect demonstrations or the inability of imitation learning algorithms to accurately infer the expert's policies.

IMITATION LEARNING

1
05 Aug 2020

Interactive Imitation Learning in State-Space

2 Aug 2020sjauhri/Interactive-Learning-in-State-space

Imitation Learning techniques enable programming the behavior of agents through demonstrations rather than manual engineering.

IMITATION LEARNING

3
02 Aug 2020

TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning

28 Jul 2020benchoi93/TrajGAIL

A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data and, thus, produce synthetic vehicle trajectories similar to real vehicle trajectories with limited observations.

IMITATION LEARNING

2
28 Jul 2020

BabyAI 1.1

24 Jul 2020mila-iqia/babyai

This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90. 4 %.

IMITATION LEARNING

466
24 Jul 2020

Learning Object Relation Graph and Tentative Policy for Visual Navigation

ECCV 2020 xiaobaishu0097/ECCV-VN

Aiming to improve these two components, this paper proposes three complementary techniques, object relation graph (ORG), trial-driven imitation learning (IL), and a memory-augmented tentative policy network (TPN).

IMITATION LEARNING REPRESENTATION LEARNING VISUAL NAVIGATION

9
21 Jul 2020

IALE: Imitating Active Learner Ensembles

9 Jul 2020crispchris/IALE

Active learning (AL) prioritizes the labeling of the most informative data samples.

ACTIVE LEARNING IMITATION LEARNING

4
09 Jul 2020

Policy learning with partial observation and mechanical constraints for multi-person modeling

7 Jul 2020PO-MC-model-NeurIPS2020/PO-MC-model

Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields.

IMITATION LEARNING

0
07 Jul 2020