# Multiagent environment crashes when more than two agents are initiated **URL:** \ **Category:** RLlib\ **Created:** [February 27, 2023, 2:53pm UTC](https://discuss.ray.io/t/multiagent-environment-crashes-when-more-than-two-agents-are-initiated/9556 "2023-02-27T14:53:10Z")\ **Posts on this page:** 2\ **Page:** 1
**Author:** ![fruzti](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/fruzti/32/3980_2.png) [@fruzti](https://discuss.ray.io/u/fruzti)\ **Post date:** [February 27, 2023, 2:53pm UTC](https://discuss.ray.io/t/multiagent-environment-crashes-when-more-than-two-agents-are-initiated/9556/1 "2023-02-27T14:53:10Z")
**How severe does this issue affect your experience of using Ray?** - High: It blocks me to complete my task. Hi everyone, Im trying to implement a multiagent environment with PPO. For some reason, when I try my implementation considering only two agents, I do not get any problem (for the tested number of train rounds), however, when I consider more than two agents, suddenly I get an error saying that my structures don’t have the same number of elements and the particualr worker crashes. I have the feeling that I’m doing something wrong, but so far I’m not being able to figure out what it is. I thought that it was because I don’t return the right dictionaries at every step but I think I do -only active agents. I’m not sure if this a problem because of my Repeated space or because I missing something basic. Below is part of the code I’m running that gives me the error with more than two agents. I appreciate if someone has some comment on it (y) class MultiAgentBaseEnv(MultiAgentEnv, ABC): ``` def __init__ (self, *args, **kwargs): self._initial_conditions() self._agent_ids = set(self.agents) @abstractmethod def _initial_conditions(self): pass @abstractmethod def _get_agent_observation(self, agent_id): pass @abstractmethod def _get_agent_reward(self, agent_id): pass @abstractmethod def _get_agent_done(self, agent_id): pass @abstractmethod def _step(self, action): pass @abstractmethod def _are_agents_done(self,dones,acting_agent_ids): pass def _render(self,*args, **kwargs): pass def reset(self): self._initial_conditions() self._agent_ids = set(self.agents) return self.agent_observations(self._agent_ids) def _get_property(self, property_name : str, agent_ids : list = None): agent_ids = self._agent_ids if agent_ids is None else agent_ids if property_name == "observations": property_fnc = self._get_agent_observation elif property_name == "rewards": property_fnc = self._get_agent_reward elif property_name == "dones": property_fnc = self._get_agent_done elif property_name == "action_sample": property_fnc = lambda x : self.action_space.sample() elif property_name == "observation_sample": property_fnc = lambda x : self.observation_space.sample() else: raise('Unsupported Property') return {i : property_fnc(i) for i in agent_ids if i != " __all__"} def agent_observations(self, agent_ids): agent_ids = [agent_ids] if not hasattr(agent_ids," __len__") else agent_ids return self._get_property("observations", agent_ids) def agent_rewards(self, agent_ids): agent_ids = [agent_ids] if not hasattr(agent_ids," __len__") else agent_ids return self._get_property("rewards", agent_ids) def agent_dones(self, agent_ids): agent_ids = [agent_ids] if not hasattr(agent_ids," __len__") else agent_ids return self._get_property("dones", agent_ids) def _acting_agents(self,action): return [key for key in action.keys() if key != " __all__"] def step(self, action): self._step(action) acting_agent_ids = self._acting_agents(action) dones = self.agent_dones(acting_agent_ids) dones[" __all__"] = self._are_agents_done(dones,acting_agent_ids) observations = self.agent_observations(acting_agent_ids) rewards = self.agent_rewards(acting_agent_ids) return observations, rewards, dones, self.info def render(self, *args, **kwargs): self._render(*args, **kwargs) def action_space_sample(self, agent_ids: list = None): agent_ids = self._agent_ids if agent_ids is None else agent_ids agent_ids = [agent_ids] if not hasattr(agent_ids," __len__") else agent_ids return self._get_property("action_sample",agent_ids) def action_space_contains(self, actions: dict) -> bool: return all([self.action_space.contains(action) for key,action in actions.items() if key != " __all__"]) def observation_space_sample(self, agent_ids : list = None): agent_ids = self._agent_ids if agent_ids is None else agent_ids agent_ids = [agent_ids] if not hasattr(agent_ids," __len__") else agent_ids return self._get_property("observation_sample",agent_ids) def observation_space_contains(self, obs: dict) -> bool: return all([self.observation_space.contains(ob) for ob in obs.values()]) ``` ` And this the class that I use for training ` class SomeClassMulti(MultiAgentBaseEnv): ``` def __init__ (self, env_config : dict): # # # ------- # ------- # ------- self.action_space = spaces.Dict( { "a" : spaces.Box(low=-1,high=1,shape=(1,)), "b" : spaces.Box(low=-1,high=1,shape=(1,)), "c" : spaces.Discrete(self.n_mod) } ) self.observation_space = spaces.Dict( { "action_history" : Repeated( spaces.Dict( { "a" : spaces.Box(low=-1,high=1,shape=(1,)), "b" : spaces.Box(low=-1,high=1,shape=(1,)), "c" : spaces.Discrete(self.n_mod) } ), max_len=self.N_max ), "avg_neighbors_history" : Repeated( spaces.Dict( { "a" : spaces.Box(low=-1,high=1,shape=(1,)), "b" : spaces.Box(low=-1,high=1,shape=(1,)), "c" : spaces.Box(low=0,high=self.n_mod,shape=(1,)) } ), max_len=self.N_max ) } ) super(). __init__ () def _initial_conditions(self): self.agents = {i : {key : [] for key in self.observation_space.sample()} for i in range(self.n_agents)} self.things= {i : Something(self.ParamA, self.lt, params= {"a" : [], "b" : [], "c" : []}) for i in range(self.n_agents)} self.info = {} self._curr_reward = None def _get_agent_observation(self, agent_id): return self.agents[agent_id] def _get_agent_reward(self, agent_id): return self._curr_reward def _get_agent_done(self, agent_id): return self.things[agent_id].n == self.N_max def _are_agents_done(self,dones,acting_agent_ids): duration_exceeded = sum([self.things[agent_id].duration for agent_id in self.things.keys()]) >= self.MAX return all(dones.values()) or duration_exceeded def _step(self, actions): scaling = self.n_agents- 1 acting_agent_ids = self._acting_agents(actions) action_aggregator = { key : np.array([0.0],dtype=np.float32) for key in self.action_space.sample().keys()} for agent_id in acting_agent_ids: action = actions[agent_id] a= self._get_a(action["a"]) if self.waveforms[agent_id].n_pulses == 0: a= 0 b= self._get_b(action["b"]) c= self._get_c(action["c"]) self.things[agent_id].add(a,b,c) self.agents[agent_id]["action_history"].append(action) action_aggregator = {key : value + action[key]/scaling for key,value in action_aggregator.items()} for agent_id in acting_agent_ids: action = actions[agent_id] avg_neighbors_history = {key : value - action[key]/scaling for key,value in action_aggregator.items()} self.agents[agent_id]["avg_neighbors_history"].append(avg_neighbors_history) dones = self.agent_dones(acting_agent_ids) episode_done = self._are_agents_done(dones,[]) if episode_done: target= self.target() self._curr_reward = target else: self._curr_reward = 0 def _get_a(self, x): return float(self.a_min + (self.a_max - self.a_min)*(x + 1)/2) def _get_b(self, x): return float(self.b_min + (self.b_max - self.b_min)*(x + 1)/2) def _get_c(self,x): return x def target(self,targetMax): output = sum([sum(thing.params['a']) for thing in self.things.values()]) def _render(self, *args, **kwargs): raise ValueError("Not implemented") ``` ` And this part of how I run the environment: config = { “train\_batch\_size”: 256, # 4000 “rollout\_fragment\_length” : 42, # 200 “lr”: 0.001, # 5e-5 “env”: env\_to\_use, “env\_config”: env\_config, “model”: { “fcnet\_hiddens”: [32, 16], “fcnet\_activation”: “relu”, }, “num\_workers”: 8, “framework”: “torch”, “log\_level”: “ERROR”, “disable\_env\_checking” : False, } ppo\_config = DEFAULT\_CONFIG.copy() ppo\_config.update(config) trainer = PPOTrainer(config=ppo\_config, env=env\_to\_use) num\_epochs = 100 for run\_index in range(num\_epochs): print(f"Starting run {run\_index}…“) if run\_index%10 == 0: path = trainer.save() start = time.time() result = trainer.train() end = time.time() print(f"Training time: {end-start} [s]”) ---
**Author:** ![arturn](https://sea2.discourse-cdn.com/flex020/user_avatar/discuss.ray.io/arturn/32/2096_2.png) [@arturn](https://discuss.ray.io/u/arturn)\ **Post date:** [April 17, 2023, 5:38pm UTC](https://discuss.ray.io/t/multiagent-environment-crashes-when-more-than-two-agents-are-initiated/9556/2 "2023-04-17T17:38:12Z")
I think the code is incomplete. I can’t find `self.n_agents`. Can you please paste a complete script that is also well formatted such that someone else can simply copy, paste and run it?