Browse > Methodology > Imitation Learning

Imitation Learning

33 papers with code · Methodology

State-of-the-art leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Latest papers with code

Go-Explore: a New Approach for Hard-Exploration Problems

30 Jan 2019Dorozhko-Anton/go-explore

Go-Explore can also harness human-provided domain knowledge and, when augmented with it, scores a mean of over 650k points on Montezuma's Revenge. On Pitfall, Go-Explore with domain knowledge is the first algorithm to score above zero.

IMITATION LEARNING MONTEZUMA'S REVENGE

30 Jan 2019

ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst

7 Dec 2018aidriver/ChauffeurNet

Our goal is to train a policy for autonomous driving via imitation learning that is robust enough to drive a real vehicle. We find that standard behavior cloning is insufficient for handling complex driving scenarios, even when we leverage a perception system for preprocessing the input and a controller for executing the output on the car: 30 million examples are still not enough.

AUTONOMOUS DRIVING IMITATION LEARNING

07 Dec 2018

On the stability analysis of optimal state feedbacks as represented by deep neural models

6 Dec 2018darioizzo/neurostability

Research has shown how the optimal feedback control of several non linear systems of interest in aerospace applications can be represented by deep neural architectures and trained using techniques including imitation learning, reinforcement learning and evolutionary algorithms. Such deep architectures are here also referred to as Guidance and Control Networks, or G&CNETs.

IMITATION LEARNING

06 Dec 2018

Mapping Navigation Instructions to Continuous Control Actions with Position-Visitation Prediction

10 Nov 2018clic-lab/drif

We propose an approach for mapping natural language instructions and raw observations to continuous control of a quadcopter drone. Our model predicts interpretable position-visitation distributions indicating where the agent should go during execution and where it should stop, and uses the predicted distributions to select the actions to execute.

CONTINUOUS CONTROL IMITATION LEARNING

10 Nov 2018

Differentiable MPC for End-to-end Planning and Control

NeurIPS 2018 locuslab/differentiable-mpc

We present foundations for using Model Predictive Control (MPC) as a differentiable policy class for reinforcement learning in continuous state and action spaces. This provides one way of leveraging and combining the advantages of model-free and model-based approaches.

IMITATION LEARNING

31 Oct 2018

Neural Modular Control for Embodied Question Answering

26 Oct 2018abhshkdz/House3D

Independent reinforcement learning at each level of hierarchy enables sub-policies to adapt to consequences of their actions and recover from errors. Subsequent joint hierarchical training enables the master policy to adapt to the sub-policies.

EMBODIED QUESTION ANSWERING IMITATION LEARNING QUESTION ANSWERING

26 Oct 2018

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

ICLR 2019 akanimax/Variational_Discriminator_Bottleneck

Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. 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 Oct 2018

Imitation Learning for Neural Morphological String Transduction

EMNLP 2018 ZurichNLP/emnlp2018-imitation-learning-for-neural-morphology

We employ imitation learning to train a neural transition-based string transducer for morphological tasks such as inflection generation and lemmatization. Previous approaches to training this type of model either rely on an external character aligner for the production of gold action sequences, which results in a suboptimal model due to the unwarranted dependence on a single gold action sequence despite spurious ambiguity, or require warm starting with an MLE model.

IMITATION LEARNING

31 Aug 2018

Multi-Agent Generative Adversarial Imitation Learning

NeurIPS 2018 ermongroup/multiagent-gail

Imitation learning algorithms can be used to learn a policy from expert demonstrations without access to a reward signal. However, most existing approaches are not applicable in multi-agent settings due to the existence of multiple (Nash) equilibria and non-stationary environments.

IMITATION LEARNING

26 Jul 2018

Bipedal Walking Robot using Deep Deterministic Policy Gradient

16 Jul 2018nav74neet/ddpg4biped

The control systems community has started to show interest towards several machine learning algorithms from the sub-domains such as supervised learning, imitation learning and reinforcement learning to achieve autonomous control and intelligent decision making. The results show that the bipedal walking pattern had similar characteristics to that of a human walking pattern.

DECISION MAKING IMITATION LEARNING

16 Jul 2018