5 citations · 10 across the 11 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…