• cd into the od_mstar3 folder.
• python3 setup.py build_ext --inplace
• Check by going back to the root of the git folder, running python3 and "import cpp_mstar"

• Pick appropriate number of meta agents via variables NUM_META_AGENTS and NUM_IL_META_AGENTS in parameters.py
• The number of RL meta-agents is implicity defined by the difference between total meta-agents and IL meta-agents (NUM_RL_META_AGENTS = NUM_META_AGENTS - NUM_IL_META_AGENTS)
• Name training run via training_version in parameters.py
• call python driver.py

• parameters.py - Training parameters.
• driver.py - Driver of program. Holds global network for A3C.
• Runner.py - Compute node for training. Maintains a single meta agent.
• Worker.py - A single agent in a simulation environment. Majority of episode computation, including gradient calculation, occurs here.
• Ray_ACNet.py - Defines network architecture.
• Env_Builder.py - Defines the lower level structure of the Lifelong MAPF environment for PRIMAL2, including the world and agents class.
• PRIMAL2Env.py - Defines the high level environment class.
• Map_Generator2.py - Algorithm used to generate worlds, parameterized by world size, obstacle density and wall components.
• PRIMAL2Observer.py - Defines the decentralized observation of each PRIMAL2 agent.
• Obsever_Builder.py - The high level observation class

• fully trained PRIMAL2 model in one-shot environment - https://www.dropbox.com/s/3nppkpy7psg0j5v/model_PRIMAL2_oneshot_3astarMaps.7z?dl=0
• fully trained PRIMAL2 model in LMAPF environment - https://www.dropbox.com/s/6wjq2bje4mcjywj/model_PRIMAL2_continuous_3astarMaps.7z?dl=0

Mehul Damani

Zhiyao Luo

Emerson Wenzel

Guillaume Sartoretti