5 citations · 10 across the 11 of their papers we have counts for
11 papers
Accelerating Policy Gradient by Estimating Value Function from Prior Computation in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
This paper investigates the use of prior computation to estimate the value function to improve sample efficiency in on-policy policy gradient methods in reinforcement learning. Our…
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Hassam Sheikh, Kizza Frisbee, Mariano Phielipp
Application of ensemble of neural networks is becoming an imminent tool for advancing the state-of-the-art in deep reinforcement learning algorithms. However, training these large…
Minimizing Communication while Maximizing Performance in Multi-Agent Reinforcement Learning
Varun Kumar Vijay, Hassam Sheikh, Somdeb Majumdar +1
Inter-agent communication can significantly increase performance in multi-agent tasks that require co-ordination to achieve a shared goal. Prior work has shown that it is possible…
Learning Intrinsic Symbolic Rewards in Reinforcement Learning
Hassam Sheikh, Shauharda Khadka, Santiago Miret +1
Learning effective policies for sparse objectives is a key challenge in Deep Reinforcement Learning (RL). A common approach is to design task-related dense rewards to improve task…
Automatic Feature Extraction, Categorization and Detection of Malicious Code in Android Applications
Muhammad Zuhair Qadir, Atif Nisar Jilani, Hassam Ullah Sheikh
Since Android has become a popular software platform for mobile devices recently; they offer almost the same functionality as personal computers. Malwares have also become a big co…
Preventing Value Function Collapse in Ensemble {Q}-Learning by Maximizing Representation Diversity
Hassam Ullah Sheikh, Ladislau Bölöni
The classic DQN algorithm is limited by the overestimation bias of the learned Q-function. Subsequent algorithms have proposed techniques to reduce this problem, without fully elim…
Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward
Hassam Ullah Sheikh, Ladislau Bölöni
Many cooperative multi-agent problems require agents to learn individual tasks while contributing to the collective success of the group. This is a challenging task for current sta…
Universal Policies to Learn Them All
Hassam Ullah Sheikh, Ladislau Bölöni
We explore a collaborative and cooperative multi-agent reinforcement learning setting where a team of reinforcement learning agents attempt to solve a single cooperative task in a…
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning
Hassam Ullah Sheikh, Ladislau Bölöni
We are considering a scenario where a team of bodyguard robots provides physical protection to a VIP in a crowded public space. We use deep reinforcement learning to learn the poli…
The Emergence of Complex Bodyguard Behavior Through Multi-Agent Reinforcement Learning
Hassam Ullah Sheikh, Ladislau Bölöni
In this paper we are considering a scenario where a team of robot bodyguards are providing physical protection to a VIP in a crowded public space. We show that the problem involves…
Emergence of Scenario-Appropriate Collaborative Behaviors for Teams of Robotic Bodyguards
Hassam Ullah Sheikh, Ladislau Boloni
We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders. This tas…