680 citations · 929 across the 11 of their papers we have counts for
13 papers
Robust Risk-Sensitive Reinforcement Learning Agents for Trading Markets
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 cost…
CDT: Cascading Decision Trees for Explainable Reinforcement Learning
Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding +2
Deep Reinforcement Learning (DRL) has recently achieved significant advances in various domains. However, explaining the policy of RL agents still remains an open problem due to se…
Work in Progress: Temporally Extended Auxiliary Tasks
Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal +1
Predictive auxiliary tasks have been shown to improve performance in numerous reinforcement learning works, however, this effect is still not well understood. The primary purpose o…
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
Chao Gao, Bilal Kartal, Pablo Hernandez-Leal +1
How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the r…
Action Guidance with MCTS for Deep Reinforcement Learning
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. In this paper, we focus on how to use action guida…
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
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 inefficien…
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning
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. Insp…
Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
Chao Gao, Pablo Hernandez-Leal, Bilal Kartal +1
The Pommerman Team Environment is a recently proposed benchmark which involves a multi-agent domain with challenges such as partial observability, decentralized execution (without…
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Bilal Kartal, Pablo Hernandez-Leal, Chao Gao +1
Safe reinforcement learning has many variants and it is still an open research problem. Here, we focus on how to use action guidance by means of a non-expert demonstrator to avoid…
An Exchange Mechanism to Coordinate Flexibility in Residential Energy Cooperatives
Shantanu Chakraborty, Pablo Hernandez-Leal, Michael Kaisers
Energy cooperatives (ECs) such as residential and industrial microgrids have the potential to mitigate increasing fluctuations in renewable electricity generation, but only if thei…
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
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. However, there are still several challenges…
A Survey and Critique of Multiagent Deep Reinforcement Learning
Pablo Hernandez-Leal, Bilal Kartal, Matthew E. Taylor
Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has led to a dramatic increase in the number of applications and methods. Recent works have…
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
Pablo Hernandez-Leal, Michael Kaisers, Tim Baarslag +1
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…