Beeler, Chris
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Beeler, Chris, Subramanian, Sriram Ganapathi, Sprague, Kyle, Chatti, Nouha, Bellinger, Colin, Shahen, Mitchell, Paquin, Nicholas, Baula, Mark, Dawit, Amanuel, Yang, Zihan, Li, Xinkai, Crowley, Mark, Tamblyn, Isaac
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by taking actions in the real world is infeasible and possibly dangerous. Moreover, chemical processing and discovery involves challenges which are not commonly found in RL benchmarks and therefore offer a rich space to work in. We introduce a set of highly customizable and open-source RL environments, ChemGymRL, based on the standard Open AI Gym template. ChemGymRL supports a series of interconnected virtual chemical benches where RL agents can operate and train. The paper introduces and details each of these benches using well-known chemical reactions as illustrative examples, and trains a set of standard RL algorithms in each of these benches. Finally, discussion and comparison of the performances of several standard RL methods are provided in addition to a list of directions for future work as a vision for the further development and usage of ChemGymRL.
Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment
Beeler, Chris, Li, Xinkai, Crowley, Mark, Fraser, Maia, Tamblyn, Isaac
In an MDP, the state of the system is known, however, Uncertainty creates a major obstacle in solving control in a POMDP it must be estimated, leading to some problems. The goal of these problems is to construct a policy amount of uncertainty. Much of the difficulty in solving that is expected to produce optimal trajectories. In some a POMDP stems from estimating the state of the system cases, uncertainty only causes deviations from the optimal before choosing an action. This is where the majority of trajectory, which may still result in an acceptable solution.