Have me in your project？

[chauvinfish]

I am a university student in China.I am also interested in ML. I am not very experienced, it's my first time play with pysc2.I do get some basic concepts and debugging skills……

[DevonCurrent]

Yes, I am happy to have help on this project and I have invited you as a collaborator. The code I have so far is mostly from [https://github.com/skjb/pysc2-tutorial]. I used the Zerg tutorial (which is the most up-to-date example of the API) and included the Q-learning model that he provided from the sparse learning. I would recommend checking it out, as he includes tutorials explaining what he is doing step-by-step. Also, I'm happy to answer any questions you might have.

This is my first experience with ML (and I am still fairly new to github and programming in general). So, if there is anything I do that you notice is wrong, feel free to correct me!

[chauvinfish]

I am developing a zerg agent based on skjb's refined_phrase_agent.
I choose zerg because zerg depend less on building position(p and t need to block pass with buildings)
and zerg can produce a great many unit in one time.

[chauvinfish]

I would be appreaciated if you can give me your contact address.
For privacy consideration, you can send your email address or telegram id to my email address 'chauvinfish@gmail.com'

[chauvinfish]

i have seen you script. it seems that you abandoned the 'hot square' and 'green square' states in
current_states.
I don't think the way the original author handles it is very high.
But we need to figure out a way to add enemy possition into current_states or our agent will always be blind and deaf

[chauvinfish]

And our agent need to find the correct possition for second base.
It's hard to locate the central possition of a mineral field with .mean() since you will get the coordinates of mineral fields in the whole map

[chauvinfish]

The current bot is still a scripted bot rather than a true ai.
I am spending whole day writing "smart action" description, interpreting actions into underlying operation.
A true ai should output underlying operation directly if it can understand how the game works.

[chauvinfish]

It seems that my zerg agent only learnt to create as many as overlord as possible,
:)

[DevonCurrent]

I'm not sure I understand the hot_square and green_square not being in the current_states. I have this code for the hot squares (and another section for the green):
for i in range(0, 4): current_state[i + 4] = hot_squares[i]
Does this not work? I do agree that this could be handled better so that the agent has less states.

[DevonCurrent]

I've been trying to work on the agent finding the second base... I think I can have it check the minimap and look for the closest minerals nearby, and then move the camera to those mineral locations.

[DevonCurrent]

Do you mean that the bot is scripted since it has the 'sparse_agent_data' file? I'm not sure I understand how the agent would be able to learn through reinforcement without keeping track of a Q-learning table to save all of the states, actions, and rewards.

Also, is there any ML books (in English) that you would recommend that I read for this project?

[chauvinfish]

I called it scripted bot because its RL module cannot output agent actions like

1/move_camera (1/minimap [64, 64]) 2/select_point (6/select_point_act [4]; 0/screen [84, 84]) 3/select_rect (7/select_add [2]; 0/screen [84, 84]; 2/screen2 [84, 84]) 4/select_control_group (4/control_group_act [5]; 5/control_group_id [10]) 5/select_unit (8/select_unit_act [4]; 9/select_unit_id [500]) 6/select_idle_worker (10/select_worker [4]) 7/select_army (7/select_add [2]) 8/select_warp_gates (7/select_add [2]) 9/select_larva () 10/unload (12/unload_id [500]) 11/build_queue (11/build_queue_id [10]) 12/Attack_screen (3/queued [2]; 0/screen [84, 84]) 13/Attack_minimap (3/queued [2]; 1/minimap [64, 64]) 14/Attack_Attack_screen (3/queued [2]; 0/screen [84, 84]) 15/Attack_Attack_minimap (3/queued [2]; 1/minimap [64, 64]) 16/Attack_AttackBuilding_screen (3/queued [2]; 0/screen [84, 84]) 17/Attack_AttackBuilding_minimap (3/queued [2]; 1/minimap [64, 64]) 18/Attack_Redirect_screen (3/queued [2]; 0/screen [84, 84]) 19/Scan_Move_screen (3/queued [2]; 0/screen [84, 84]) 20/Scan_Move_minimap (3/queued [2]; 1/minimap [64, 64]).

It depend human labor to interpret 'smart action' into agent action

smart_actions = [ ACTION_DO_NOTHING, ACTION_SELECT_PROBE, ACTION_BUILD_PYLON, ACTION_BUILD_GATEWAY, ACTION_SELECT_GATEWAY, ACTION_BUILD_ZEALOT, ACTION_SELECT_ARMY, ACTION_ATTACK, ]

[chauvinfish]

There is no RL book for sc2 currently. Because not many people are using pysc2 as deeplearning environment.

[chauvinfish]

If there is a RL book in Chinese on a unpopular subject, it must be translated into Chinese.

[DevonCurrent]

Oh ok, I think I understand. So would something like this be better to replace all of the elif statements at the end?
smart_action = random.choice(actions.FUNCTIONS); if self.can_do(obs, smart_action.id): actions.smart_action
This would seem to pass all the possible functions, so then we would just need to add the correct arguments to each choice.

[DevonCurrent]

yeah, I figured there was nothing for sc2. I was just wondering if there was any books in general that you would recommend. I do have a couple books that go over RL for python that I will probably start looking through.