What the turn action means?

Actions are discrete actions. They can be move, attack and turn.
The move and attack action can be comprehend easily.
However, what does turn means?
I check the frames in render. I found the turn action correspond half-line. So I am confused with the turn action.
Hope for your reply.

ps. It seems Positive rewards(>0) are better than negative rewards(<0), is that right?
In training process, which is more important? The rewards or the penalties?

By default direction system is disabled because we found it is hard for agents to learn.

MAgent/src/gridworld/GridWorld.cc

Line 21 in 92256aa

turn_mode = false;

Originally we designed our agent with a direction and a sector view range. But we found agents cannot learn how to deal with directions (You know in a map, it is even hard for a human being.)
So in all our examples, direction and turn actions are disabled. Every agent faces to north and has a circular range.

Thank you for your reply. It seems that the framework defines Agent's 12 move actions in a world coordinate, which is enough to use.
If a agent has a view range , how can I obtain the ID of agents within the view range of this agent?
It seems env.get_observation() do not have this information.

You can use

MAgent/examples/train_battle.py

Lines 64 to 65 in 92256aa

    
           obs[i] = env.get_observation(handles[i]) 
        
           ids[i] = env.get_agent_id(handles[i])

Thanks for your reply. However, obs[i] = env.get_observation(handles[i]) is something like
array([[ 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 1. , 0. , 0. , -0.105,
0.408, 0.456],
, which seem do not have ID information.
ids[i] = env.get_agent_id(handles[i]):
('id: ', array([25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41,
42, 43, 44, 45, 46, 47, 48, 49], dtype=int32))
, which is all agent's ID.
Therefore, env.get_observation and env.get_agent_id can not help to find the IDs that in view range.
Do you have a better way?

How can I obtain the velocity information of one agent?

Where can I add( or reduce or modified) the original actions in code?
Hope your reply for the above questions. Thank you very much.

I can only point some related code to you. You have to modify c++ code. Hopefully you are okay with c++.

You can write your own function to fit your case. You can find some examples here

MAgent/src/gridworld/GridWorld.cc

Line 709 in 92256aa

void GridWorld::get_info(GroupHandle group, const char *name, void *void_buffer) {

. Then you can use python interface to call them.

Another method is that we can get positions of all agents by

MAgent/python/magent/gridworld.py

Lines 361 to 368 in 92256aa

    
               def get_pos(self, handle): 
        
                   """ get position of agents in a group 
        
                   Returns 
        
                   ------- 
        
                   pos: numpy array (int) 
        
                       the shape of pos is (n, 2) 
        
                   """

. And then calculate in python, i.e. calculate the distance pairs and compare them with the radius of view range)

The velocity is 'speed' in the c++ code.

MAgent/src/gridworld/AgentType.cc

Lines 29 to 36 in 92256aa

    
           AgentType::AgentType(int n, std::string name, const char **keys, float *values, bool turn_mode) { 
        
               this->name = name; 
        
               // default value 
        
               attack_in_group = false; 
        
               width = length = 1; 
        
               speed = 1.0; hp = 1.0;

We first call

MAgent/src/gridworld/GridWorld.cc

Line 403 in 92256aa

void GridWorld::set_action(GroupHandle group, const int *actions) {

to set actions and then execute the actions in

MAgent/src/gridworld/GridWorld.cc

Line 456 in 92256aa

void GridWorld::step(int *done) {

Thanks for your reply.
1.I have already calculated the distance pairs and compared them with the radius of view range. I just concern the compute efficiency issue.
2.I notice here: speed = 1.0.However, it is the setting speed not the current speed. For example, when a agent hit a wall or other agents, its speed may turn to zero. Furthermore, when a agent hit the right-side wall and the action order is still towards to the right direction, its speed will also be zero. The current speed information seem to be ignored. It might be important to know whether a agent become static or not.
3.C++ is fine, In temp_c_booster.cc, function get_action:
int get_action(const std::pair<int, int> &disp, bool stride) {
int action = -1;
if (disp.first < 0) {
if (disp.second < 0) {
action = 1;
} else if (disp.second == 0) {
action = stride ? 0 : 2;
} else {
action = 3;
}
} else if (disp.first == 0) {
if (disp.second < 0) {
action = stride ? 4 : 5;
} else if (disp.second == 0) {
action = 6;
} else {
action = stride ? 8 : 7;
}
} else {
if (disp.second < 0) {
action = 9;
} else if (disp.second == 0) {
action = stride ? 12 : 10;
} else {
action = 11;
}
}
return action;
}

seems only have move actions. I still have not found the attack action. Can someone turnoff the attack actions?
4.Have MAgent concern the wall hitting penality? How can I obtain it?
Thanks for your patience.

Well, I have found the action space layout.
In AgentType.cc
Line 118:
int n_action = attack_base + attack_range->get_count();
for (int i = 0; i < n_action; i++) {
action_space.push_back(i);
}
Cause n_action=21, so there are (0,1,2,...20) 21 actions in total.

	obs[i] = env.get_observation(handles[i])
	ids[i] = env.get_agent_id(handles[i])

	def get_pos(self, handle):
	""" get position of agents in a group

	Returns
	-------
	pos: numpy array (int)
	the shape of pos is (n, 2)
	"""


	AgentType::AgentType(int n, std::string name, const char *keys, float values, bool turn_mode) {
	this->name = name;

	// default value
	attack_in_group = false;
	width = length = 1;
	speed = 1.0; hp = 1.0;