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Imitation Learning

45 papers with code · Methodology

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Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

ICLR 2019 Xue Bin Peng et al

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.

CONTINUOUS CONTROL IMAGE GENERATION IMITATION LEARNING

01 May 2019

Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor

ICLR 2019 Te-Lin Wu et al

A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning.

IMITATION LEARNING META-LEARNING

01 May 2019

CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication

ICLR 2019 Nikita Kitaev et al

The game involves two players: a Teller and a Drawer.

IMITATION LEARNING

01 May 2019

Generative predecessor models for sample-efficient imitation learning

ICLR 2019 Yannick Schroecker et al

We propose Generative Predecessor Models for Imitation Learning (GPRIL), a novel imitation learning algorithm that matches the state-action distribution to the distribution observed in expert demonstrations, using generative models to reason probabilistically about alternative histories of demonstrated states.

IMITATION LEARNING

01 May 2019

Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning

ICLR 2019 Ilya Kostrikov et al

We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework.

IMITATION LEARNING

01 May 2019

Learning Exploration Policies for Navigation

ICLR 2019 Tao Chen et al

Numerous past works have tackled the problem of task-driven navigation.

IMITATION LEARNING

01 May 2019

Generative Adversarial Self-Imitation Learning

ICLR 2019 Junhyuk Oh et al

This paper explores a simple regularizer for reinforcement learning by proposing Generative Adversarial Self-Imitation Learning (GASIL), which encourages the agent to imitate past good trajectories via generative adversarial imitation learning framework.

IMITATION LEARNING

01 May 2019

Visual Imitation with a Minimal Adversary

ICLR 2019 Scott Reed et al

The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.

IMITATION LEARNING

01 May 2019

Adversarial Exploration Strategy for Self-Supervised Imitation Learning

ICLR 2019 Zhang-Wei Hong et al

Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.

IMITATION LEARNING

01 May 2019

Deep Imitative Models for Flexible Inference, Planning, and Control

ICLR 2019 Nicholas Rhinehart et al

Model-based reinforcement learning (MBRL) offers considerably more flexibility, since a predictive model learned from data can be used to achieve various goals at test time.

AUTONOMOUS DRIVING IMITATION LEARNING

01 May 2019