Search Results for author: Riashat Islam

Found 18 papers, 8 papers with code

Importance of Empirical Sample Complexity Analysis for Offline Reinforcement Learning

no code implementations31 Dec 2021 Samin Yeasar Arnob, Riashat Islam, Doina Precup

We hypothesize that empirically studying the sample complexity of offline reinforcement learning (RL) is crucial for the practical applications of RL in the real world.

Offline RL reinforcement-learning

Offline Policy Optimization with Variance Regularization

no code implementations1 Jan 2021 Riashat Islam, Samarth Sinha, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Zhaoran Wang, Animesh Garg, Lihong Li, Doina Precup

Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.

Continuous Control Offline RL +1

Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning

no code implementations11 Dec 2019 Riashat Islam, Raihan Seraj, Samin Yeasar Arnob, Doina Precup

Furthermore, in cases where the reward function is stochastic that can lead to high variance, doubly robust critic estimation can improve performance under corrupted, stochastic reward signals, indicating its usefulness for robust and safe reinforcement learning.

Continuous Control reinforcement-learning +1

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

Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods

no code implementations11 Dec 2019 Riashat Islam, Raihan Seraj, Pierre-Luc Bacon, Doina Precup

In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution.

Policy Gradient Methods

Off-Policy Policy Gradient Algorithms by Constraining the State Distribution Shift

no code implementations16 Nov 2019 Riashat Islam, Komal K. Teru, Deepak Sharma, Joelle Pineau

This data distribution shift between current and past samples can significantly impact the performance of most modern off-policy based policy optimization algorithms.

Continuous Control

Transfer Learning by Modeling a Distribution over Policies

1 code implementation9 Jun 2019 Disha Shrivastava, Eeshan Gunesh Dhekane, Riashat Islam

Exploration and adaptation to new tasks in a transfer learning setup is a central challenge in reinforcement learning.

reinforcement-learning Transfer Learning

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.

Exploring Restart Distributions

no code implementations27 Nov 2018 Arash Tavakoli, Vitaly Levdik, Riashat Islam, Christopher M. Smith, Petar Kormushev

We consider the generic approach of using an experience memory to help exploration by adapting a restart distribution.

Bayesian Policy Gradients via Alpha Divergence Dropout Inference

1 code implementation6 Dec 2017 Peter Henderson, Thang Doan, Riashat Islam, David Meger

Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult.

Continuous Control Policy Gradient Methods

Alpha-Divergences in Variational Dropout

no code implementations12 Nov 2017 Bogdan Mazoure, Riashat Islam

We investigate the use of alternative divergences to Kullback-Leibler (KL) in variational inference(VI), based on the Variational Dropout \cite{kingma2015}.

Variational Inference

Deep Reinforcement Learning that Matters

5 code implementations19 Sep 2017 Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, David Meger

In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL).

reinforcement-learning

Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control

1 code implementation10 Aug 2017 Riashat Islam, Peter Henderson, Maziar Gomrokchi, Doina Precup

We investigate and discuss: the significance of hyper-parameters in policy gradients for continuous control, general variance in the algorithms, and reproducibility of reported results.

Continuous Control Policy Gradient Methods +1

Deep Bayesian Active Learning with Image Data

3 code implementations ICML 2017 Yarin Gal, Riashat Islam, Zoubin Ghahramani

In this paper we combine recent advances in Bayesian deep learning into the active learning framework in a practical way.

Active Learning

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