Imitation Learning
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Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
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.
Learning from Noisy Demonstration Sets via Meta-Learned Suitability Assessor
A noisy and diverse demonstration set may hinder the performances of an agent aiming to acquire certain skills via imitation learning.
CoDraw: Collaborative Drawing as a Testbed for Grounded Goal-driven Communication
The game involves two players: a Teller and a Drawer.
Generative predecessor models for sample-efficient imitation learning
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.
Discriminator-Actor-Critic: Addressing Sample Inefficiency and Reward Bias in Adversarial Imitation Learning
We identify two issues with the family of algorithms based on the Adversarial Imitation Learning framework.
Learning Exploration Policies for Navigation
Numerous past works have tackled the problem of task-driven navigation.
Generative Adversarial Self-Imitation Learning
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.
Visual Imitation with a Minimal Adversary
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.
Adversarial Exploration Strategy for Self-Supervised Imitation Learning
Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each other.
Deep Imitative Models for Flexible Inference, Planning, and Control
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.