Research to help build a better financial future
Our researchers undertake fundamental and applied research and use state-of-the-art ML to address some of the biggest challenges facing the financial services industry today and in the future.
Our widely published research covers a broad range of topics including Reinforcement learning, Natural Language Processing and Time Series Modeling. Our research is made freely available to support the AI community.
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Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition
The Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019
Authors:
C. Gao,
P. Hernandez-Leal,
B. Kartal,
M. E. Taylor
Metatrace Actor-Critic: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning
International Joint Conference on Artificial Intelligence (IJCAI), 2019
Authors:
K. Young,
B. Wang,
M. E. Taylor
A Survey and Critique of Multiagent Deep Reinforcement Learning
Journal of Autonomous Agents and Multiagent Systems (JAAMAS), 2019
Authors:
P. Hernandez-Leal,
B. Kartal,
M. E. Taylor
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Workshop on Adaptive Learning Agents (AAMAS), 2019
Authors:
B. Kartal,
P. Hernandez-Leal,
C. Gao,
M. E. Taylor
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Workshop on Reinforcement Learning in Games (AAAI), 2019
Authors:
B. Kartal,
P. Hernandez-Leal,
M. E. Taylor
Skill Reuse in Partially Observable Multiagent Environments
Workshop on Latinx in AI Coalition (NeurIPS), 2018
Authors:
P. Hernandez-Leal,
B. Kartal,
M. E. Taylor
Improving Reinforcement Learning with Human Input
International Joint Conference on Artificial Intelligence (IJCAI), 2018
Authors:
M. E. Taylor