AAMAS Conference 2023 Conference Paper
Multi-Robot Warehouse Optimization: Leveraging Machine Learning for Improved Performance
- Mara Cairo
- Bevin Eldaphonse
- Payam Mousavi
- Sahir
- Sheikh Jubair
- Matthew E. Taylor
- Graham Doerksen
- Nikolai Kummer
Author name cluster
Sahir
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
2 papers
1 author row
Possible papers
2AAAI Conference 2021 Conference Paper
Towered Actor Critic For Handling Multiple Action Types In Reinforcement Learning For Drug Discovery
- Sai Krishna Gottipati
- Yashaswi Pathak
- Boris Sattarov
- Sahir
- Rohan Nuttall
- Mohammad Amini
- Matthew E. Taylor
- Sarath Chandar
Reinforcement learning (RL) has made significant progress in both abstract and real-world domains, but the majority of state-of-the-art algorithms deal only with monotonic actions. However, some applications require agents to reason over different types of actions. Our application simulates reactionbased molecule generation, used as part of the drug discovery pipeline, and includes both uni-molecular and bi-molecular reactions. This paper introduces a novel framework, towered actor critic (TAC), to handle multiple action types. The TAC framework is general in that it is designed to be combined with any existing RL algorithms for continuous action space. We combine it with TD3 to empirically obtain significantly better results than existing methods in the drug discovery setting. TAC is also applied to RL benchmarks in OpenAI Gym and results show that our framework can improve, or at least does not hurt, performance relative to standard TD3.