Working with my own customized env

[HawkQ]

Hi, I'm trying to use this library to solve some modified multi-UAV problems, but I'm not professional in coding 🤣
Will there be any instructions about how to use pure modified environments in the future? I've read the add_new_env.py but still not clear with it😥

开发者您好，我试图用这个库去解决一些多无人机的MARL问题，但因为我并非本专业，add_new_env.py文件对我自定义环境起到的帮助极其有限，请问未来是否有更详细或进一步的说明性文档？谢谢！

[Aequatio-Space]

You can use an example from marllib/envs/base_env to check how a custom environment is wrapped for RLlib. I write my own environment according to such example.

MARLlib/marllib/envs/base_env/mpe.py

Lines 94 to 153 in 368c617

	class RLlibMPE(MultiAgentEnv):
	def __init__(self, env_config):
	map = env_config["map_name"]
	env_config.pop("map_name", None)
	env = REGISTRY[map](**env_config)
	# keep obs and action dim same across agents
	# pad_action_space_v0 will auto mask the padding actions
	env = ss.pad_observations_v0(env)
	env = ss.pad_action_space_v0(env)
	self.env = ParallelPettingZooEnv(env)
	self.action_space = self.env.action_space
	self.observation_space = GymDict({"obs": Box(
	low=-100.0,
	high=100.0,
	shape=(self.env.observation_space.shape[0],),
	dtype=self.env.observation_space.dtype)})
	self.agents = self.env.agents
	self.num_agents = len(self.agents)
	env_config["map_name"] = map
	self.env_config = env_config
	def reset(self):
	original_obs = self.env.reset()
	obs = {}
	for i in self.agents:
	obs[i] = {"obs": original_obs[i]}
	return obs
	def step(self, action_dict):
	o, r, d, info = self.env.step(action_dict)
	rewards = {}
	obs = {}
	for key in action_dict.keys():
	rewards[key] = r[key]
	obs[key] = {
	"obs": o[key]
	}
	dones = {"__all__": d["__all__"]}
	return obs, rewards, dones, info
	def close(self):
	self.env.close()
	def render(self, mode=None):
	self.env.render()
	time.sleep(0.05)
	return True
	def get_env_info(self):
	env_info = {
	"space_obs": self.observation_space,
	"space_act": self.action_space,
	"num_agents": self.num_agents,
	"episode_limit": 25,
	"policy_mapping_info": policy_mapping_dict
	}
	return env_info

[ChenJiangxi]

I'm having the same problem, and I think the key to customizing your environment is in this statement:
self.env = ParallelPettingZooEnv(env)
But I don't really know how to access my own simulation environment

[Theohhhu]

Thank you to @Aequatio-Space for offering guidance. MARLlib utilizes a standard agent-environment interface based on RLlib. A practical approach to integrating your own environment is to begin by identifying a similar environment (e.g., with the same data structure or interaction style) and then follow the integration approach used for that environment.