Search Results for author: Zafarali Ahmed

Found 12 papers, 6 papers with code

Learning how to Interact with a Complex Interface using Hierarchical Reinforcement Learning

no code implementations21 Apr 2022 Gheorghe Comanici, Amelia Glaese, Anita Gergely, Daniel Toyama, Zafarali Ahmed, Tyler Jackson, Philippe Hamel, Doina Precup

While the native action space is completely intractable for simple DQN agents, our architecture can be used to establish an effective way to interact with different tasks, significantly improving the performance of the same DQN agent over different levels of abstraction.

Hierarchical Reinforcement Learning reinforcement-learning

Temporally Abstract Partial Models

1 code implementation NeurIPS 2021 Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, Doina Precup

Humans and animals have the ability to reason and make predictions about different courses of action at many time scales.

AndroidEnv: A Reinforcement Learning Platform for Android

2 code implementations27 May 2021 Daniel Toyama, Philippe Hamel, Anita Gergely, Gheorghe Comanici, Amelia Glaese, Zafarali Ahmed, Tyler Jackson, Shibl Mourad, Doina Precup

We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem.

reinforcement-learning

Training a First-Order Theorem Prover from Synthetic Data

no code implementations5 Mar 2021 Vlad Firoiu, Eser Aygun, Ankit Anand, Zafarali Ahmed, Xavier Glorot, Laurent Orseau, Lei Zhang, Doina Precup, Shibl Mourad

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.

Automated Theorem Proving

What can I do here? A Theory of Affordances in Reinforcement Learning

1 code implementation ICML 2020 Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel, Doina Precup

Gibson (1977) coined the term "affordances" to describe the fact that certain states enable an agent to do certain actions, in the context of embodied agents.

reinforcement-learning

Learning to Prove from Synthetic Theorems

no code implementations19 Jun 2020 Eser Aygün, Zafarali Ahmed, Ankit Anand, Vlad Firoiu, Xavier Glorot, Laurent Orseau, Doina Precup, Shibl Mourad

A major challenge in applying machine learning to automated theorem proving is the scarcity of training data, which is a key ingredient in training successful deep learning models.

Automated Theorem Proving

Marginalized State Distribution Entropy Regularization in Policy Optimization

no code implementations11 Dec 2019 Riashat Islam, Zafarali Ahmed, Doina Precup

Entropy regularization is used to get improved optimization performance in reinforcement learning tasks.

Continuous Control

Transfer and Exploration via the Information Bottleneck

1 code implementation ICLR 2019 Anirudh Goyal, Riashat Islam, DJ Strouse, Zafarali Ahmed, Hugo Larochelle, Matthew Botvinick, Yoshua Bengio, Sergey Levine

In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.

InfoBot: Transfer and Exploration via the Information Bottleneck

no code implementations30 Jan 2019 Anirudh Goyal, Riashat Islam, Daniel Strouse, Zafarali Ahmed, Matthew Botvinick, Hugo Larochelle, Yoshua Bengio, Sergey Levine

In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.

Understanding the impact of entropy on policy optimization

1 code implementation27 Nov 2018 Zafarali Ahmed, Nicolas Le Roux, Mohammad Norouzi, Dale Schuurmans

Entropy regularization is commonly used to improve policy optimization in reinforcement learning.

reinforcement-learning

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