- Developed an expert planner with help of multi-agent path planning algorithm, M* and A*
- The planner was constructed with respect to the constraints of the environment. A level wise approach was adopted to address the constraints.
- We then tried to do learn multi-agent policies by trying to imitate the expert planner.
- We performed experiments to train policies of agents by imitating the expert planner.
- Our experiments failed, the agents were not able to learn by imitation. On further analysis, we figured out that there was a problem with with the way in which we were performing imitation learning. The agents were not able to assign proper credit to their actions.
