no code implementations • 16 Jul 2021 • Yue Gao, Kry Yik Chau Lui, Pablo Hernandez-Leal
Trading markets represent a real-world financial application to deploy reinforcement learning agents, however, they carry hard fundamental challenges such as high variance and costly exploration.
1 code implementation • 15 Nov 2020 • Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding, Changjian Li, Ruitong Huang
As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.
no code implementations • 1 Apr 2020 • Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Our overall conclusions are that TD-AE increases the robustness of the A2C algorithm to the trajectory length and while promising, further study is required to fully understand the relationship between auxiliary task prediction timescale and the agent's performance.
no code implementations • 26 Jul 2019 • Chao Gao, Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman.
no code implementations • 25 Jul 2019 • Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency.
no code implementations • 24 Jul 2019 • Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficiency.
no code implementations • 22 Jul 2019 • Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
In this paper we explore how actor-critic methods in deep reinforcement learning, in particular Asynchronous Advantage Actor-Critic (A3C), can be extended with agent modeling.
1 code implementation • 20 Apr 2019 • Chao Gao, Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without communication), and very sparse and delayed rewards.
no code implementations • 10 Apr 2019 • Bilal Kartal, Pablo Hernandez-Leal, Chao GAO, Matthew E. Taylor
In this paper, we shed light into the reasons behind this failure by exemplifying and analyzing the high rate of catastrophic events (i. e., suicides) that happen under random exploration in this domain.
no code implementations • 30 Nov 2018 • Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power.
no code implementations • 12 Oct 2018 • Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
The primary goal of this article is to provide a clear overview of current multiagent deep reinforcement learning (MDRL) literature.
no code implementations • 28 Jul 2017 • Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag, Enrique Munoz de Cote
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves.