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How to use list_agents method in tempest
Best Python code snippet using tempest_python
train_lio.py
Source:train_lio.py 
1"""Trains LIO agents on Escape Room game.23Three versions of LIO:41. LIO built on top of policy gradient52. LIO built on top of actor-critic63. Fully decentralized version of LIO on top of policy gradient7"""89from __future__ import division10from __future__ import print_function1112import argparse13import json14import os15import random1617import numpy as np18import tensorflow as tf1920from lio.alg import config_ipd_lio21from lio.alg import config_room_lio22from lio.alg import evaluate23from lio.env import ipd_wrapper24from lio.env import room_symmetric252627def train(config):2829 seed = config.main.seed30 np.random.seed(seed)31 random.seed(seed)32 tf.set_random_seed(seed)3334 dir_name = config.main.dir_name35 exp_name = config.main.exp_name36 log_path = os.path.join('..', 'results', exp_name, dir_name)37 model_name = config.main.model_name38 save_period = config.main.save_period3940 os.makedirs(log_path, exist_ok=True)4142 # Keep a record of parameters used for this run43 with open(os.path.join(log_path, 'config.json'), 'w') as f:44 json.dump(config, f, indent=4, sort_keys=True)4546 n_episodes = int(config.alg.n_episodes)47 n_eval = config.alg.n_eval48 period = config.alg.period4950 epsilon = config.lio.epsilon_start51 epsilon_step = (52 epsilon - config.lio.epsilon_end) / config.lio.epsilon_div5354 if config.env.name == 'er':55 env = room_symmetric.Env(config.env)56 elif config.env.name == 'ipd':57 env = ipd_wrapper.IPD(config.env)5859 if config.lio.decentralized:60 from lio_decentralized import LIO61 elif config.lio.use_actor_critic:62 from lio_ac import LIO63 else:64 from lio_agent import LIO6566 list_agents = []67 for agent_id in range(env.n_agents):68 if config.lio.decentralized:69 list_agent_id_opp = list(range(env.n_agents))70 del list_agent_id_opp[agent_id]71 list_agents.append(LIO(config.lio, env.l_obs, env.l_action,72 config.nn, 'agent_%d' % agent_id,73 config.env.r_multiplier, env.n_agents,74 agent_id, list_agent_id_opp))75 else:76 list_agents.append(LIO(config.lio, env.l_obs, env.l_action,77 config.nn, 'agent_%d' % agent_id,78 config.env.r_multiplier, env.n_agents,79 agent_id)) 8081 for agent_id in range(env.n_agents):82 if config.lio.decentralized:83 list_agents[agent_id].create_opp_modeling_op()84 else:85 list_agents[agent_id].receive_list_of_agents(list_agents)86 list_agents[agent_id].create_policy_gradient_op()87 list_agents[agent_id].create_update_op()88 if config.lio.use_actor_critic:89 list_agents[agent_id].create_critic_train_op()9091 for agent_id in range(env.n_agents):92 list_agents[agent_id].create_reward_train_op()9394 # This handles the special case of two asymmetric agents,95 # one of which is the reward-giver and the other is the recipient96 if config.lio.asymmetric:97 assert config.env.n_agents == 298 for agent_id in range(env.n_agents):99 list_agents[agent_id].set_can_give(100 agent_id != config.lio.idx_recipient)101102 config_proto = tf.ConfigProto()103 if config.main.use_gpu:104 config_proto.device_count['GPU'] = 1105 config_proto.gpu_options.allow_growth = True106 else:107 config_proto.device_count['GPU'] = 0108 sess = tf.Session(config=config_proto)109 sess.run(tf.global_variables_initializer())110111 if config.lio.use_actor_critic:112 for agent in list_agents:113 sess.run(agent.list_initialize_v_ops)114115 list_agent_meas = []116 if config.env.name == 'er':117 list_suffix = ['reward_total', 'n_lever', 'n_door',118 'received', 'given', 'r-lever', 'r-start', 'r-door']119 elif config.env.name == 'ipd':120 list_suffix = ['given', 'received', 'reward_env',121 'reward_total']122 for agent_id in range(1, env.n_agents + 1):123 for suffix in list_suffix:124 list_agent_meas.append('A%d_%s' % (agent_id, suffix))125126 saver = tf.train.Saver(max_to_keep=config.main.max_to_keep)127128 header = 'episode,step_train,step,'129 header += ','.join(list_agent_meas)130 if config.env.name == 'er':131 header += ',steps_per_eps\n'132 else:133 header += '\n'134 with open(os.path.join(log_path, 'log.csv'), 'w') as f:135 f.write(header) 136137 step = 0138 step_train = 0139 for idx_episode in range(1, n_episodes + 1):140141 list_buffers = run_episode(sess, env, list_agents, epsilon,142 prime=False)143 step += len(list_buffers[0].obs)144145 if config.lio.decentralized:146 for idx, agent in enumerate(list_agents):147 agent.train_opp_model(sess, list_buffers,148 epsilon)149150 for idx, agent in enumerate(list_agents):151 agent.update(sess, list_buffers[idx], epsilon)152153 list_buffers_new = run_episode(sess, env, list_agents,154 epsilon, prime=True)155 step += len(list_buffers_new[0].obs)156157 for agent in list_agents:158 if agent.can_give:159 agent.train_reward(sess, list_buffers,160 list_buffers_new, epsilon)161162 for idx, agent in enumerate(list_agents):163 if config.lio.decentralized:164 agent.train_opp_model(sess, list_buffers_new,165 epsilon)166 else:167 agent.update_main(sess)168169 step_train += 1170171 if idx_episode % period == 0:172173 if config.env.name == 'er':174 (reward_total, n_move_lever, n_move_door, rewards_received,175 rewards_given, steps_per_episode, r_lever,176 r_start, r_door) = evaluate.test_room_symmetric(177 n_eval, env, sess, list_agents)178 matrix_combined = np.stack([reward_total, n_move_lever, n_move_door,179 rewards_received, rewards_given,180 r_lever, r_start, r_door])181 elif config.env.name == 'ipd':182 given, received, reward_env, reward_total = evaluate.test_ipd(183 n_eval, env, sess, list_agents)184 matrix_combined = np.stack([given, received, reward_env,185 reward_total])186187 s = '%d,%d,%d' % (idx_episode, step_train, step)188 for idx in range(env.n_agents):189 s += ','190 if config.env.name == 'er':191 s += ('{:.3e},{:.3e},{:.3e},{:.3e},{:.3e},'192 '{:.3e},{:.3e},{:.3e}').format(193 *matrix_combined[:, idx])194 elif config.env.name == 'ipd':195 s += '{:.3e},{:.3e},{:.3e},{:.3e}'.format(196 *matrix_combined[:, idx])197 if config.env.name == 'er':198 s += ',%.2f\n' % steps_per_episode199 else:200 s += '\n'201 with open(os.path.join(log_path, 'log.csv'), 'a') as f:202 f.write(s)203204 if idx_episode % save_period == 0:205 saver.save(sess, os.path.join(log_path, '%s.%d'%(206 model_name, idx_episode)))207208 if epsilon > config.lio.epsilon_end:209 epsilon -= epsilon_step210211 saver.save(sess, os.path.join(log_path, model_name))212 213214def run_episode(sess, env, list_agents, epsilon, prime=False):215 list_buffers = [Buffer(env.n_agents) for _ in range(env.n_agents)]216 list_obs = env.reset()217 done = False218219 while not done:220 list_actions = []221 for agent in list_agents:222 action = agent.run_actor(list_obs[agent.agent_id], sess,223 epsilon, prime)224 list_actions.append(action)225226 list_rewards = []227 total_reward_given_to_each_agent = np.zeros(env.n_agents)228 for agent in list_agents:229 if agent.can_give:230 reward = agent.give_reward(list_obs[agent.agent_id],231 list_actions, sess)232 else:233 reward = np.zeros(env.n_agents)234 reward[agent.agent_id] = 0235 total_reward_given_to_each_agent += reward236 reward = np.delete(reward, agent.agent_id)237 list_rewards.append(reward)238239 if env.name == 'er':240 list_obs_next, env_rewards, done = env.step(list_actions, list_rewards)241 elif env.name == 'ipd':242 list_obs_next, env_rewards, done = env.step(list_actions)243244 for idx, buf in enumerate(list_buffers):245 buf.add([list_obs[idx], list_actions[idx], env_rewards[idx],246 list_obs_next[idx], done])247 buf.add_r_from_others(total_reward_given_to_each_agent[idx])248 buf.add_action_all(list_actions)249 if list_agents[idx].include_cost_in_chain_rule:250 buf.add_r_given(np.sum(list_rewards[idx]))251252 list_obs = list_obs_next253254 return list_buffers255256257class Buffer(object):258259 def __init__(self, n_agents):260 self.n_agents = n_agents261 self.reset()262263 def reset(self):264 self.obs = []265 self.action = []266 self.reward = []267 self.obs_next = []268 self.done = []269 self.r_from_others = []270 self.r_given = []271 self.action_all = []272273 def add(self, transition):274 self.obs.append(transition[0])275 self.action.append(transition[1])276 self.reward.append(transition[2])277 self.obs_next.append(transition[3])278 self.done.append(transition[4])279280 def add_r_from_others(self, r):281 self.r_from_others.append(r)282283 def add_action_all(self, list_actions):284 self.action_all.append(list_actions)285286 def add_r_given(self, r):287 self.r_given.append(r)288289290if __name__ == '__main__':291292 parser = argparse.ArgumentParser()293 parser.add_argument('exp', type=str, choices=['er', 'ipd'])294 args = parser.parse_args()295296 if args.exp == 'er':297 config = config_room_lio.get_config()298 elif args.exp == 'ipd':299 config = config_ipd_lio.get_config()300
...
maptoTF.py
Source:maptoTF.py
...99--> if agent_less_type = 1, then types 1, 2, 3, 4, 5, ... will be deployed100--> if agent_less_type = 2, then types 1, 3, 5, ... will be deployed101--> if agent_less_type = 3, then types 1, 4, 7, ... will be deployed102'''103def get_list_agents(agent_grid, nb_types, final_resolution, agent_spaced=1, agent_less_type=1):104 result = []105 grid2d_length = len(agent_grid)106 id_point = 0107 id_agent = 0108 for x in range(grid2d_length):109 for y in range(grid2d_length):110 if agent_grid[x][y] and x % agent_spaced == 0 and y % agent_spaced == 0:111 for i in range(1, nb_types + 1, agent_less_type):112 element_agent = []113 point = []114 point.append(x)115 point.append(y)116 element_agent.append(point)117 element_agent.append(id_point)118 element_agent.append(get_type_agent(i,final_resolution)[0])119 element_agent.append(get_type_agent(i,final_resolution)[1])120 element_agent.append(get_type_agent(i,final_resolution)[2])121 element_agent.append(id_agent)122 result.append(element_agent)123 id_agent += 1124 id_point += 1125 return result126'''127population_grid is the population density grid128The function returns list_skills, where each skill is a 2-vector: (point, population)129'''130def get_list_skills(population_grid):131 result = []132 grid2d_length = len(population_grid)133 for x in range(grid2d_length):134 for y in range(grid2d_length):135 if population_grid[x][y] > 0:136 element_skill = []137 point = []138 point.append(x)139 point.append(y)140 element_skill.append(point)141 element_skill.append(population_grid[x][y])142 result.append(element_skill)143 return result144def get_id_skill(point, list_skills):145 list_skills_length = len(list_skills)146 for i in range(list_skills_length):147 if list_skills[i][0][0] == point[0] and list_skills[i][0][1] == point[1]:148 return i149 return -1150'''151agent_grid is a True-False grid, True iff an antenna can be deployed152population_grid is the population density grid153nb_types: the number of antenna types we want (not more than 7)154The function returns agents_to_skills, an array associating each agent id to a list of skill ids155'''156def get_agents_to_skills(agent_grid, population_grid, nb_types, list_agents, list_skills):157 result = []158 grid2d_length = len(agent_grid)159 nb_agents = len(list_agents)160 for id_agent in range(nb_agents):161 list_skills_for_id_agent = []162 neighbor_list = points_around(list_agents[id_agent][0], list_agents[id_agent][4], grid2d_length, True)163 for neighbor in neighbor_list:164 id_skill = get_id_skill(neighbor, list_skills)165 # here, if id_skill == -1 this means that point neighbor has no population166 if id_skill >= 0:167 list_skills_for_id_agent.append(id_skill)168 result.append(list_skills_for_id_agent)169 return result170'''171Based on agents_to_skills we make a new array filtering list_agents172'''173def remove_agents_with_no_skill_from_list_agents(list_agents, agents_to_skills):174 result = []175 new_id_agent = 0176 init_nb_agents = len(agents_to_skills)177 for i in range(init_nb_agents):178 if agents_to_skills[i]:179 list_agents[i][5] = new_id_agent180 new_id_agent += 1181 result.append(list_agents[i])182 return result183'''184Return a filtered list of agents_to_skills removing agents with no skill185'''186def remove_agents_with_no_skill_from_agents_to_skills(agents_to_skills):187 result = []188 init_nb_agents = len(agents_to_skills)189 for i in range(init_nb_agents):190 if agents_to_skills[i]:191 result.append(agents_to_skills[i])192 return result193##'''194##agent_grid is a True-False grid, True iff an antenna can be deployed195##population_grid is the population density grid196##nb_types: the number of antenna types we want (not more than 7)197##The function creates:198##list_agents: each agent is a 4-vector: (point, cost1, cost2, range)199##list_skills: each skill is a 2-vector: (point, population)200##agents_to_skills: an array associating each agent id to a list of skill ids201##'''202##def map_to_TF(agent_grid, population_grid, nb_types, list_agents, list_skills, agents_to_skills):203## list_agents = []204## list_skills = []205## agents_to_skills = []206##207## # computes first the list of agents using agent_grid208## # list_agents = get_list_agents(agent_grid, nb_types)209##210## # computes now the list of skills using population_grid211## # list_skills = get_list_skills(population_grid)212##213## # computes the mapping agents_to_skills214##...
TFtofile.py
Source:TFtofile.py
1##'''2##converts the input lists into a TF instance in a file (new version)3##'''4##def TFtofile(file_name, list_agents, list_skills, agents_to_skills, dim):5## nb_agents = len(list_agents)6## nb_skills = len(list_skills)7## file_name = "TF-" + file_name + ".txt"8## f = open(file_name, 'wt')9## f.write('P ')10## f.write('%s %s %s\n' % (int(nb_agents), int(nb_skills), int(dim)))11## # print skills12## for id_skill in range(nb_skills):13## f.write('%s (%s %s) %s\n' % (int(id_skill), int(list_skills[id_skill][0][0]), int(list_skills[id_skill][0][1]), int(list_skills[id_skill][1])))14## # print agents to costs and skills15## for id_agent in range(nb_agents):16## f.write('%s ' % int(id_agent))17## f.write('(%s %s)' % (int(list_agents[id_agent][0][0]), int(list_agents[id_agent][0][1])))18## # cost1 (deployment cost)19## f.write(' %s' % int(list_agents[id_agent][2]))20## # cost2 (repair cost)21## f.write(' %s' % int(list_agents[id_agent][3]))22## for id_skill in agents_to_skills[id_agent]:23## f.write(' %s' % int(id_skill))24## f.write('\n')25## f.close()26'''27returns the number of different points on the map where an antenna can be deployed28'''29def get_nb_id_points(list_agents):30 length_list = len(list_agents)31 return list_agents[length_list - 1][1] + 132'''33returns the list of agents placed at the same point on the map, where the id of the point is id_point34'''35def get_list_agents_at_id_point(list_agents, id_point):36 nb_agents = len(list_agents)37 result = []38 for id_agent in range(nb_agents):39 if list_agents[id_agent][1] == id_point:40 result.append(list_agents[id_agent])41 return result42'''43converts the input lists into a TF instance in a file .tf (last version)44in addition, it creates another file .info which gives the correspondance45between each agent id and its coordinates on the map: each line is46a <id_agent> <cost1> <cost2> <range> <num_line> <num_col>47Recall that list_agents is an arry where each agent is a 6-vector: (point, id_point, cost1, cost2, range, id_agent)48'''49def TFtofile(file_name, list_agents, list_skills, agents_to_skills, dim):50 nb_agents = len(list_agents)51 nb_skills = len(list_skills)52 f = open(file_name + '.tf', 'wt')53 f_info = open(file_name + '.info', 'wt')54 f.write('p ')55 f.write('%s %s\n' % (int(nb_agents), int(nb_skills)))56 f_info.write(f'p {int(nb_agents)}\n')57 # agents58 for id_agent in range(nb_agents):59 f.write('a %s %s %s' % (int(id_agent), int(list_agents[id_agent][2]), int(list_agents[id_agent][3])))60 for id_skill in agents_to_skills[id_agent]:61 f.write(' %s' % int(id_skill))62 f.write('\n')63 f_info.write(f'a {int(id_agent)} {int(list_agents[id_agent][2])} {int(list_agents[id_agent][3])} {int(list_agents[id_agent][4])} {list_agents[id_agent][0][0]} {list_agents[id_agent][0][1]}\n')64 # skills65 f_info.close()66 for id_skill in range(nb_skills):67 f.write('s %s %s\n' % (int(id_skill), int(list_skills[id_skill][1])))68 # exclusion constraints69 #nb_id_points = get_nb_id_points(list_agents)70## print('nb_id_points: ', nb_id_points)71 #if nb_id_points != nb_agents:72 # for id_point in range(nb_id_points):73 # list_agents_at_id_point = get_list_agents_at_id_point(list_agents, id_point)74 # nb_agents_at_id_point = len(list_agents_at_id_point)75 # if nb_agents_at_id_point > 1:76 # f.write('e')77 # for id_agent in range(nb_agents_at_id_point):78 # f.write(' %s' % int(list_agents_at_id_point[id_agent][5]))79 # f.write('\n')80 f.close()81'''82converts the input lists into a TF instance in a file (simple version)83'''84def TFtofilesimple(file_name, list_agents, list_skills, agents_to_skills, dim):85 nb_agents = len(list_agents)86 nb_skills = len(list_skills)87 file_name = file_name + ".tf.txt"88 f = open(file_name, 'wt')89 f.write('P ')90 f.write('%s %s\n' % (int(nb_agents), int(nb_skills)))91 for id_agent in range(nb_agents):92 f.write('%s' % int(list_agents[id_agent][2]))93 for id_skill in agents_to_skills[id_agent]:94 f.write(' %s' % int(id_skill))95 f.write('\n')96 f.close()97'''98file to save the points according to agent ids99The format is:100First line: <nb_agents> <dim_grid>101For each subsequent line: x_coord y_coord range102'''103def TFtomapagentidstopointsrange(file_name, list_agents, dim):104 nb_agents = len(list_agents)105 file_name = file_name + ".idtoinfo.txt"106 f = open(file_name, 'wt')107 f.write('%s %s\n' % (len(list_agents), dim))108 for id_agent in range(nb_agents):109 f.write('%s %s %s\n' % (int(list_agents[id_agent][0][0]), int(list_agents[id_agent][0][1]), int(list_agents[id_agent][4])))...
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