Comments (6)
https://github.com/shariqiqbal2810/multiagent-particle-envs
Shariq has added walls in his version of MPE.
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in environment.py there seem to be some chances to define range (low/high), please check.
This is for action_spaces (forces)
u_action_space = spaces.Box(low=-agent.u_range, high=+agent.u_range, shape=(world.dim_p,), dtype=np.float32)
Furthurmore, for the environment shown while rendering, the below exists in rendering.py:
def set_bounds(self, left, right, bottom, top):
which is used in environment.py as below:
self.viewers[i].set_bounds(pos[0]-cam_range,pos[0]+cam_range,pos[1]-cam_range,pos[1]+cam_range)
but for setting a real boundary that confines agents during simulation, you should manipulate the done value returned by step function. Please refer to issue#10
We are trying to add boundaries to the environment, but when we check the code of the entity in core.py, there is only the size property, from our understanding, we can only create circle object. Are there any solutions to create other shape?
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https://github.com/shariqiqbal2810/multiagent-particle-envs
Shariq has added walls in his version of MPE.
That's definitely great. Is there any way to make the walls visible to the agents, rather than being invisible entities that cannot be crossed?
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@emanuelepesce did you manage to place the wall objects around the rendering window in order to limit the agents from running to infinity?
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@Kimonili to limit the agents from running to infinity you can use the wall class defined by Shariq (multiagent) as suggested by @HassamSheikh .
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Related Issues (20)
- raise NotImplementedError
- FileNotFoundError: [Errno 2] No such file or directory: 'C:\\Users\\Jarvis\\Desktop\\r-maac-main\\multiagent\\scenarios\\spread_collect.py'
- This code base is no longer maintained HOT 3
- the code about good_agents & adversaries at simple_tag.py
- What is the input form of the action in the‘simple_reference’? HOT 2
- If want to change the scenario from simple to other, how should I do? HOT 1
- Centralized learning-decentralized execution clarification (engineering perspective)
- how to restore the game process after so many times of episodes?
- Turn the environment into 3D HOT 2
- Questions about agent`s state information in MADDPG HOT 1
- Running timeout!
- cant run different scenarios except simply.py HOT 1
- NN code
- Use the checkpoint file to continue training
- How to delete a landmark when training HOT 1
- Index error: list index out of range
- Questions about simple_spread agent actions
- Add image as background
- RuntimeWarning: invalid value encountered in logaddexp
- Wrong reward in simple speaker listener HOT 1
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