diff --git a/.gitignore b/.gitignore index a392053..1aadcf6 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,2 @@ -models/* \ No newline at end of file +models/* +old/* \ No newline at end of file diff --git a/results/airsim_trained.csv b/results/airsim_trained.csv deleted file mode 100644 index 5b05737..0000000 --- a/results/airsim_trained.csv +++ /dev/null @@ -1,6 +0,0 @@ -k, reward, variance -1, -3.769855862084762, 0.8687310840666437 -2, -3.0496015778904457, 0.6895091085245685 -3, -2.72592842597965, 0.5699835200316272 -4, -2.854071901804428, 0.3669829882371786 - diff --git a/results/airsim_trained2.csv b/results/airsim_trained2.csv deleted file mode 100644 index f215b1a..0000000 --- a/results/airsim_trained2.csv +++ /dev/null @@ -1,6 +0,0 @@ -k, reward, std -1, -6.1264006465977925, 2.6261114461138306 -2, -3.4652359381486413, 1.3084831858508834 -3, -2.9150179750214456, 1.6347459314744373 -4, -2.591131502530944, 0.5262619174971477 - diff --git a/results/dt.csv b/results/dt.csv deleted file mode 100644 index d6c7b5b..0000000 --- a/results/dt.csv +++ /dev/null @@ -1,25 +0,0 @@ -k, dt, reward_mean, reward_std -1, 0.1, -1270917.0522448036, 452924.35657368874 -1, 0.075, -30928.217654153723, 4563.656872878289 -1, 0.05, -14397.50841863872, 1673.3872385991804 -1, 0.025, -105830.96233101287, 34804.39674439732 -1, 0.01, -229.15979292372407, 122.16289677324487 -1, 0.0075, -226.158466412491, 117.27375648085358 -2, 0.1, -43447.18674405579, 3541.6004575584434 -2, 0.075, -32722.454417610348, 6316.180427440367 -2, 0.05, -14824.649933272547, 2258.9152495125486 -2, 0.025, -7812.444590972108, 3675.1200760901643 -2, 0.01, -50.51020781200708, 12.566522373659023 -2, 0.0075, -61.81372454136315, 17.288184423322008 -3, 0.1, -64423.94484878054, 7613.523611863424 -3, 0.075, -89274.11591375525, 55166.2073372286 -3, 0.05, -13079.297380239308, 1382.2080816048356 -3, 0.025, -2524.0317706592095, 870.7324284339057 -3, 0.01, -40.95792156471694, 4.1560902247655385 -3, 0.0075, -49.152214471131366, 4.650807822028735 -4, 0.1, -83233.46634033919, 10661.050650642606 -4, 0.075, -121482.80289824714, 126367.00902560871 -4, 0.05, -23524.480476183544, 3846.420740360492 -4, 0.025, -2419.134007699452, 1300.701048888548 -4, 0.01, -38.293215563303235, 3.1199301223969256 -4, 0.0075, -45.57409971626793, 3.5505187250842867 diff --git a/results/dt_baseline.csv b/results/dt_baseline.csv deleted file mode 100644 index b1e3b4b..0000000 --- a/results/dt_baseline.csv +++ /dev/null @@ -1,13 +0,0 @@ -dt, centralized, reward -0.1, True, -59889.33630689392, 929.1649165693375 -0.1, False, -189402.46914413854, 21044.093804338474 -0.075, True, -31807.67350082144, 224.30576245420792 -0.075, False, -108914.14745636014, 17015.96881208654 -0.05, True, -13173.11445346924, 72.96319668105578 -0.05, False, -29632.11862055091, 5781.989300153512 -0.025, True, -3147.2376625646857, 2.534888983669798 -0.025, False, -9306.91341130534, 908.9434398151692 -0.01, True, -14.899697200560103, 1.8157149806086754 -0.01, False, -6535.8762397044575, 554.0006754735136 -0.0075, True, -19.53691068723764, 2.205459220088043 -0.0075, False, -6292.478492341895, 533.2987085219571 diff --git a/results/figure_code/test_3pane.py b/results/figure_code/test_3pane.py deleted file mode 100644 index 02e1681..0000000 --- a/results/figure_code/test_3pane.py +++ /dev/null @@ -1,282 +0,0 @@ -# import pickle -# import sys -import time -# import matplotlib -import matplotlib.pyplot as plt -from matplotlib.pyplot import gca -# from matplotlib import rc -# import numpy as np -# import torch -# -# from gnn.agg_nn import make_model -# from problems.flocking import FlockingProblem - - -from os import path -import configparser -import numpy as np -import random -import gym -import gym_flock -import torch -import sys - -from learner.state_with_delay import MultiAgentStateWithDelay -from learner.gnn_dagger import DAGGER - - -# rc('text', usetex=True) -# font = {'family' : 'sans-serif', -# 'weight' : 'bold', -# 'size' : 22} -# matplotlib.rc('font', **font) - -font = {'family' : 'sans-serif', - 'weight' : 'bold', - 'size' : 14} - -# rc('text', usetex=True) -# font = {'family' : 'Times New Roman', 'weight' : 'bold', 'size' : 22} -# matplotlib.rc('font', **font) - -def main(): - - # initialize gym env - fname = 'cfg/dagger_leader.cfg' - config_file = path.join(path.dirname(__file__), fname) - config = configparser.ConfigParser() - config.read(config_file) - - if config.sections(): - section_name = config.sections()[0] - args = config[section_name] - else: - args = config[config.default_section] - - env_name = "FlockingLeader-v0" - env1 = gym.make(env_name) - env2 = gym.make(env_name) - env3 = gym.make(env_name) - - env1.env.params_from_cfg(args) - env2.env.params_from_cfg(args) - env3.env.params_from_cfg(args) - - # # use seed - seed = 1 - # env1.seed(seed) - # env2.seed(seed) - # env3.seed(seed) - random.seed(seed) - # np.random.seed(seed) - torch.manual_seed(seed) - - n_test_episodes = args.getint('n_test_episodes') - - n_leaders = 2 - - # initialize params tuple - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - - - k1 = 1 - learner1 = DAGGER(device, args, k1) - actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k1) - learner1.load_model(actor_path, device) - - k2 = 4 - learner2 = DAGGER(device, args, k2) - actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k2) - learner2.load_model(actor_path, device) - - animated_plot = True - - r_max = 10.0 - # run the experiment - if animated_plot: - plt.ion() - fig = plt.figure() - ax = fig.add_subplot(132) - line1, = ax.plot([0], [0], 'bo') # Returns a tuple of line objects, thus the comma - ax.plot([0], [0], 'kx') - plt.ylim(-1.0 * r_max, 1.0 * r_max) - plt.xlim(-1.0 * r_max, 1.0 * r_max) - # a = gca() - # a.set_xticklabels(a.get_xticks(), font) - # a.set_yticklabels(a.get_yticks(), font) - plt.title('GNN K = 1') - cost_text_nn = plt.text(-5.0, 8.5, '', fontsize=18) - quiver1 = ax.quiver([], [], [], [], color='k', scale=1.0) - - - ################ - ax = fig.add_subplot(133) - line2, = ax.plot([0], [0], 'ro') # Returns a tuple of line objects, thus the comma - ax.plot([0], [0], 'kx') - plt.ylim(-1.0 * r_max, 1.0 * r_max) - plt.xlim(-1.0 * r_max, 1.0 * r_max) - plt.title('GNN K = 4') - # a = gca() - # a.set_xticklabels(a.get_xticks(), font) - # a.set_yticklabels(a.get_yticks(), font) - cost_text_d = plt.text(-5.0, 8.5, '', fontsize=18) - quiver2 = ax.quiver([], [], [], [], color='k', scale=1.0) - - ################## - ax = fig.add_subplot(131) - line3, = ax.plot([0], [0], 'go') # Returns a tuple of line objects, thus the comma - ax.plot([0], [0], 'kx') - plt.ylim(-1.0 * r_max, 1.0 * r_max) - plt.xlim(-1.0 * r_max, 1.0 * r_max) - plt.title('Global Controller') - # a = gca() - # a.set_xticklabels(a.get_xticks(), font) - # a.set_yticklabels(a.get_yticks(), font) - cost_text_c = plt.text(-5.0, 8.5, '', fontsize=18) - quiver3 = ax.quiver([], [], [], [], color='k', scale=1.0) - - mng = plt.get_current_fig_manager() - mng.resize(*mng.window.maxsize()) - - - for ep_num in [ 0,2,5,6,7,8,9, 10, 11]: # - episode_reward1 = 0 - episode_reward2 = 0 - episode_reward3 = 0 - seed = ep_num + 15 - - env1.seed(seed) - env2.seed(seed) - env3.seed(seed) - - np.random.seed(seed) - delay_state1 = MultiAgentStateWithDelay(device, args, env1.reset(), prev_state=None, k=k1) - np.random.seed(seed) - delay_state2 = MultiAgentStateWithDelay(device, args, env2.reset(), prev_state=None, k=k2) - # env2.reset() - np.random.seed(seed) - env3.reset() - - done = False - t = 0 - - while not done: - - action = learner1.select_action(delay_state1) - next_state1, reward1, done, _ = env1.step(action.cpu().numpy()) - next_state1 = MultiAgentStateWithDelay(device, args, next_state1, prev_state=delay_state1, k=k1) - delay_state1 = next_state1 - episode_reward1 += reward1 - - # _, reward2, _, _ = env2.step(env2.env.controller(False)) - action = learner2.select_action(delay_state2) - next_state2, reward2, done, _ = env2.step(action.cpu().numpy()) - next_state2 = MultiAgentStateWithDelay(device, args, next_state2, prev_state=delay_state2, k=k2) - delay_state2 = next_state2 - - _, reward3, _, _ = env3.step(env3.env.controller(True)) - - episode_reward2 += reward2 - episode_reward3 += reward3 - - state1 = env1.env.x - state2 = env2.env.x - # state2 = env2.env.x - state3 = env3.env.x - - - # plot agent positions - if animated_plot and t % 4 == 0: - if t % 10 == 0: - cost_text_nn.set_text("Reward: {0:.2f}".format(episode_reward1)) - cost_text_d.set_text("Reward: {0:.2f}".format(episode_reward2)) - cost_text_c.set_text("Reward: {0:.2f}".format(episode_reward3)) - - line1.set_xdata(state1[:, 0]) - line1.set_ydata(state1[:, 1]) - line2.set_xdata(state2[:, 0]) - line2.set_ydata(state2[:, 1]) - line3.set_xdata(state3[:, 0]) - line3.set_ydata(state3[:, 1]) - - - s = 0.05 - quiver1.set_offsets(state1[0:n_leaders, 0:2]) - quiver1.set_UVC(state1[0:n_leaders, 2] * s, state1[0:n_leaders, 3] * s) - - quiver2.set_offsets(state2[0:n_leaders, 0:2]) - quiver2.set_UVC(state2[0:n_leaders, 2] * s, state2[0:n_leaders, 3] * s) - - - quiver3.set_offsets(state3[0:n_leaders, 0:2]) - quiver3.set_UVC(state3[0:n_leaders, 2] * s, state3[0:n_leaders, 3] * s) - - - - # ax.relim() - # ax.autoscale_view() - fig.canvas.draw() - fig.canvas.flush_events() - time.sleep(0.001) - t = t + 1 - - #env.render() - print(episode_reward1) - print(episode_reward2) - print(episode_reward3) - env1.close() - env2.close() - env3.close() - - - - # problem.num_test_traj = 500 - - # c_centr = problem.get_average_cost_expert(True) - # c_not_centr = problem.get_average_cost_expert(False) - # c_nn = problem.get_average_cost_model(model, device) - # - # print('Centralized Cost & Not Centralized Cost & NN Cost \\\\') - # print(('%.3E' % c_centr) + ' & ' + ('%.3E' % c_not_centr) + ' & ' + ('%.3E' % c_nn) + ' \\\\') - - # for n in range(0, 20): - # cost_nn = 0 - # cost_d = 0 - # cost_c = 0 - # xt1 = xt2 = xt3 = problem.initialize() - # if isinstance(problem, FlockingProblem): - # x_agg1 = np.zeros((problem.n_nodes, problem.nx * problem.filter_len, problem.n_pools)) - # else: - # x_agg1 = np.zeros((problem.n_agents * problem.nx, problem.episode_len)) - # - # for t in range(0, 250): - # - # x_agg1 = problem.aggregate(x_agg1, xt1) - # x_agg1_t = x_agg1.reshape((problem.n_nodes, problem.filter_len * problem.nx * problem.n_pools)) - # - # x_agg1_t = torch.from_numpy(x_agg1_t).to(device=device) - # ut1 = model(x_agg1_t).data.cpu().numpy().reshape(problem.n_nodes, problem.nu) - # - # # optimal controller - # problem.centralized_controller = False - # ut2 = problem.controller(xt2) - # problem.centralized_controller = True - # ut3 = problem.controller(xt3) - # - # xt1 = problem.forward(xt1, ut1) - # xt2 = problem.forward(xt2, ut2) - # xt3 = problem.forward(xt3, ut3) - # - # cost_nn = cost_nn + problem.instant_cost(xt1, ut1) * problem.dt - # cost_d = cost_d + problem.instant_cost(xt2, ut2) * problem.dt - # cost_c = cost_c + problem.instant_cost(xt3, ut3) * problem.dt - # - # # if t == 200: - # # time.sleep(3.0) - # - - - - -if __name__ == "__main__": - main() diff --git a/results/figure_code/test_one_traj.py b/results/figure_code/test_one_traj.py deleted file mode 100644 index cd335bd..0000000 --- a/results/figure_code/test_one_traj.py +++ /dev/null @@ -1,158 +0,0 @@ -from os import path -import configparser -import numpy as np -import random -import gym -import gym_flock -import torch -# import sys - -from learner.state_with_delay import MultiAgentStateWithDelay -from learner.gnn_dagger import DAGGER - -# import seaborn as sns -import matplotlib -from matplotlib import pyplot as plt - -font = {'family': 'serif', - 'weight': 'bold', - 'size': 14} - -matplotlib.rc('font', **font) - -save_dir = 'results/fig/' - - -def test(args, actor_path, k): - # initialize gym env - env_name = args.get('env') - print(env_name) - # env_name = "FlockingAirsimAccel-v0" - env = gym.make(env_name) - - if isinstance(env.env, gym_flock.envs.FlockingRelativeEnv): - env.env.params_from_cfg(args) - - # use seed - seed = args.getint('seed') - env.seed(seed) - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - - # initialize params tuple - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - learner = DAGGER(device, args, k) - n_test_episodes = args.getint('n_test_episodes') - - learner.load_model(actor_path, device) - - n_steps = 400 - - steps = np.zeros((n_steps,)) - min_dists_mean = np.zeros((n_steps,)) - min_dists_std = np.zeros((n_steps,)) - - vel_diffs_mean = np.zeros((n_steps,)) - vel_diffs_std = np.zeros((n_steps,)) - - episode_reward = 0 - state = MultiAgentStateWithDelay(device, args, env.reset(), prev_state=None, k=k) - - for step in range(0, n_steps): - action = learner.select_action(state) - next_state, reward, done, _ = env.step(action.cpu().numpy()) - - # next_state, reward, done, _ = env.step(env.env.controller(False)) - - stats = env.env.get_stats() - - min_dists_mean[step] = np.mean(stats['min_dists']) - vel_diffs_mean[step] = np.mean(stats['vel_diffs']) - min_dists_std[step] = np.std(stats['min_dists']) - vel_diffs_std[step] = np.std(stats['vel_diffs']) - - steps[step] = step - - next_state = MultiAgentStateWithDelay(device, args, next_state, prev_state=state, k=k) - episode_reward += reward - state = next_state - - if step % 300 == 0: - env.render() - plt.savefig(save_dir + 'leader_traj' + str(step) + '.eps', format='eps') - - plt.show() - - plt.ioff() - # - # y = min_dists_mean - # y_min = min_dists_mean - min_dists_std - # y_max = min_dists_mean + min_dists_std - # - # fig = plt.figure() - # plt.plot(steps, y, 'b-') - # plt.fill_between(steps, y_min, y_max, color='lightblue') - # plt.xlabel('Step') - # plt.ylabel('Min. Distances') - # plt.tight_layout() - # plt.savefig(save_dir + 'min_dist.eps', format='eps') - # plt.show() - # - # y = vel_diffs_mean - # y_min = vel_diffs_mean - vel_diffs_std - # y_max = vel_diffs_mean + vel_diffs_std - # - # fig = plt.figure() - # plt.plot(steps, y, 'r-') - # plt.fill_between(steps, y_min, y_max, color='orange') - # plt.xlabel('Step') - # plt.ylabel('Velocity Diff.') - # plt.tight_layout() - # plt.savefig(save_dir + 'vel_diff.eps', format='eps') - # plt.show() - - env.close() - - -def main(): - # fname = sys.argv[1] - - # actor_path = 'models/ddpg_actor_FlockingRelative-v0_k3' - # actor_path = 'models/ddpg_actor_FlockingRelative-v0_k3' - # actor_path = 'models/ddpg_actor_FlockingStochastic-v0_stoch2' - - # k = 3 - # actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k) - # fname = 'cfg/dagger.cfg' - - # actor_path = 'models/ddpg_actor_FlockingStochastic-v0_transfer_stoch' + str(k) - # fname = 'cfg/dagger_stoch.cfg' - - - k = 3 - actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k) - fname = 'cfg/dagger_leader.cfg' - - - - - config_file = path.join(path.dirname(__file__), fname) - config = configparser.ConfigParser() - config.read(config_file) - - printed_header = False - - if config.sections(): - for section_name in config.sections(): - if not printed_header: - print(config[section_name].get('header')) - printed_header = True - - test(config[section_name], actor_path, k) - else: - test(config[config.default_section], actor_path, k) - - -if __name__ == "__main__": - main() diff --git a/results/figure_code/test_one_traj2.py b/results/figure_code/test_one_traj2.py deleted file mode 100644 index 5c5bc28..0000000 --- a/results/figure_code/test_one_traj2.py +++ /dev/null @@ -1,183 +0,0 @@ -from os import path -import configparser -import numpy as np -import random -import gym -import gym_flock -import torch -# import sys - -from learner.state_with_delay import MultiAgentStateWithDelay -from learner.gnn_dagger import DAGGER - -# import seaborn as sns -import matplotlib -from matplotlib import pyplot as plt - -font = {'family': 'serif', - 'weight': 'bold', - 'size': 14} - -matplotlib.rc('font', **font) - -save_dir = 'results/fig/' - - -def test(args, actor_path, k): - # initialize gym env - env_name = args.get('env') - print(env_name) - # env_name = "FlockingAirsimAccel-v0" - env = gym.make(env_name) - - if isinstance(env.env, gym_flock.envs.FlockingRelativeEnv): - env.env.params_from_cfg(args) - - # use seed - seed = args.getint('seed') - - n_steps = 500 - n_test_episodes = args.getint('n_test_episodes') - - min_dists_mean = np.zeros((n_steps,)) - min_dists_std = np.zeros((n_steps,)) - vel_diffs_mean = np.zeros((n_steps,)) - vel_diffs_std = np.zeros((n_steps,)) - - - min_dists_mean2 = np.zeros((n_steps,)) - min_dists_std2 = np.zeros((n_steps,)) - vel_diffs_mean2 = np.zeros((n_steps,)) - vel_diffs_std2 = np.zeros((n_steps,)) - - steps = np.zeros((n_steps,)) - - - for method in ['gnn', 'decentralized']: - env.seed(seed) - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - - if method == 'gnn': - # initialize params tuple - device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") - learner = DAGGER(device, args, k) - learner.load_model(actor_path, device) - state = MultiAgentStateWithDelay(device, args, env.reset(), prev_state=None, k=k) - - episode_reward = 0 - - - for step in range(0, n_steps): - if method == 'gnn': - action = learner.select_action(state) - next_state, reward, done, _ = env.step(action.cpu().numpy()) - else: - next_state, reward, done, _ = env.step(env.env.controller(False)) - - # next_state, reward, done, _ = env.step(env.env.controller(True)) - - stats = env.env.get_stats() - - if method == 'gnn': - min_dists_mean[step] = np.mean(stats['min_dists']) - vel_diffs_mean[step] = np.mean(stats['vel_diffs']) - min_dists_std[step] = np.std(stats['min_dists']) - vel_diffs_std[step] = np.std(stats['vel_diffs']) - else: - min_dists_mean2[step] = np.mean(stats['min_dists']) - vel_diffs_mean2[step] = np.mean(stats['vel_diffs']) - min_dists_std2[step] = np.std(stats['min_dists']) - vel_diffs_std2[step] = np.std(stats['vel_diffs']) - - steps[step] = step - - if method == 'gnn': - next_state = MultiAgentStateWithDelay(device, args, next_state, prev_state=state, k=k) - episode_reward += reward - state = next_state - - plt.ioff() - - y = min_dists_mean - y_min = min_dists_mean - min_dists_std - y_max = min_dists_mean + min_dists_std - - y2 = min_dists_mean2 - y_min2 = min_dists_mean2 - min_dists_std2 - y_max2 = min_dists_mean2 + min_dists_std2 - - fig = plt.figure() - plt.plot(steps, y, 'b-', label='GNN') - plt.fill_between(steps, y_min, y_max, color='lightblue') - - plt.plot(steps, y2, 'r-', label='Local') - plt.fill_between(steps, y_min2, y_max2, color='orange') - - plt.axhline(y=1, linestyle='--', color='k', label='Comm. Radius') - plt.legend() - - plt.xlabel('Step') - plt.ylabel('Min. Distances') - plt.tight_layout() - plt.savefig(save_dir + 'min_dist.eps', format='eps') - plt.show() - - y = vel_diffs_mean - y_min = vel_diffs_mean - vel_diffs_std - y_max = vel_diffs_mean + vel_diffs_std - - y2 = vel_diffs_mean2 - y_min2 = vel_diffs_mean2 - vel_diffs_std2 - y_max2 = vel_diffs_mean2 + vel_diffs_std2 - - fig = plt.figure() - plt.plot(steps, y, 'b-', label='GNN') - plt.fill_between(steps, y_min, y_max, color='lightblue') - - plt.plot(steps, y2, 'r-', label='Local') - plt.fill_between(steps, y_min2, y_max2, color='orange') - plt.legend() - plt.xlabel('Step') - plt.ylabel('Velocity Diff.') - plt.tight_layout() - plt.savefig(save_dir + 'vel_diff.eps', format='eps') - plt.show() - - env.close() - - -def main(): - # fname = sys.argv[1] - - # actor_path = 'models/ddpg_actor_FlockingRelative-v0_k3' - # actor_path = 'models/ddpg_actor_FlockingRelative-v0_k3' - # actor_path = 'models/ddpg_actor_FlockingStochastic-v0_stoch2' - - k = 3 - actor_path = 'models/ddpg_actor_FlockingRelative-v0_transfer' + str(k) - fname = 'cfg/dagger.cfg' - - # actor_path = 'models/ddpg_actor_FlockingStochastic-v0_transfer_stoch' + str(k) - # fname = 'cfg/dagger_stoch.cfg' - - config_file = path.join(path.dirname(__file__), fname) - config = configparser.ConfigParser() - config.read(config_file) - - printed_header = False - - if config.sections(): - for section_name in config.sections(): - if not printed_header: - print(config[section_name].get('header')) - printed_header = True - - test(config[section_name], actor_path, k) - else: - test(config[config.default_section], actor_path, k) - - -if __name__ == "__main__": - main() diff --git a/results/hidden_size.csv b/results/hidden_size.csv deleted file mode 100644 index 8dce9df..0000000 --- a/results/hidden_size.csv +++ /dev/null @@ -1,19 +0,0 @@ -n_layers, hidden_size, reward -1, 4, -276.82078391232966, 64.29987962788128 -1, 8, -117.08710312156381, 43.270671367306626 -1, 16, -61.03456490238341, 14.140745828045532 -1, 32, -36.13962443213741, 3.0798684637230522 -1, 64, -31.05390727777222, 2.10786356396021 -1, 128, -48.76131344797435, 2.037034383138412 -2, 4, -269.382749890559, 78.82907067281965 -2, 8, -171.02713416748716, 52.41214606794095 -2, 16, -59.9277702649744, 14.771392137187954 -2, 32, -40.95792156471694, 4.1560902247655385 -2, 64, -31.700934519085287, 3.077514236703468 -2, 128, -45.93426070960081, 8.625290439145136 -3, 4, -1619.2879423585034, 258.56136124922637 -3, 8, -198.44929262433703, 58.968542460814064 -3, 16, -102.47049217642862, 42.928379013488964 -3, 32, -42.760629061376925, 6.136905519439495 -3, 64, -36.02397189682316, 11.074362519579216 -3, 128, -65.32106296773641, 33.16110632000706 diff --git a/results/n.csv b/results/n.csv deleted file mode 100644 index 9f7fa6e..0000000 --- a/results/n.csv +++ /dev/null @@ -1,21 +0,0 @@ -k, n_agents, reward -1, 50, -432.9951122485818, 249.61795362199805 -1, 75, -319.83185308018284, 130.14053520184876 -1, 100, -229.15979292372407, 122.16289677324487 -1, 125, -217.1340116775613, 95.3461704293501 -1, 150, -172.14025454122398, 51.635444445613196 -2, 50, -89.97712855183387, 108.03483725875923 -2, 75, -55.540402898312344, 11.146641164414202 -2, 100, -50.51020781200708, 12.566522373659023 -2, 125, -46.76860348867619, 12.555701772579592 -2, 150, -37.0106466777022, 3.232041202865438 -3, 50, -73.35025660496456, 109.79197822945807 -3, 75, -44.86283894212626, 4.923115098403242 -3, 100, -40.95792156471694, 4.1560902247655385 -3, 125, -41.15206481867972, 15.84355642218285 -3, 150, -34.25862769738046, 2.3905789668612885 -4, 50, -44.96090491863062, 8.438123529368031 -4, 75, -41.707591033111626, 4.70898675648138 -4, 100, -38.293215563303235, 3.1199301223969256 -4, 125, -39.02082714295042, 14.76143649629966 -4, 150, -34.26850178446582, 2.8046991044265575 diff --git a/results/n_baseline.csv b/results/n_baseline.csv deleted file mode 100644 index 1908465..0000000 --- a/results/n_baseline.csv +++ /dev/null @@ -1,11 +0,0 @@ -centralized, n_agents, reward -True, 50, -21.189087350417914, 4.914202107841018 -True, 75, -17.578174984474465, 3.725134289661792 -True, 100, -14.899697200560103, 1.8157149806086754 -True, 125, -14.497312413551816, 1.857963983379794 -True, 150, -13.436401447590432, 1.508457092067479 -False, 50, -5414.270146056273, 1222.4711829412147 -False, 75, -6172.847920185132, 791.1410941872234 -False, 100, -6535.8762397044575, 554.0006754735136 -False, 125, -7694.822410082497, 522.970998376011 -False, 150, -8332.014326499273, 589.4615515522688 diff --git a/results/old/plot_results_old.py b/results/old/plot_results_old.py deleted file mode 100644 index 7c45b1a..0000000 --- a/results/old/plot_results_old.py +++ /dev/null @@ -1,183 +0,0 @@ -import matplotlib.pyplot as plt -import csv -import numpy as np -from collections import OrderedDict - -colors={'Centralized':'green', 'Decentralized':'red', '4':'blue', '3':'darkviolet', '2':'orange', '1':'gold'} -plt.rcParams["font.family"] = "Serif" - -def get_dict(fname, k_ind, v_ind): - list_costs = OrderedDict() - with open(fname, 'r') as csvfile: - plots = csv.reader(csvfile, delimiter=',') - next(plots, None) - for row in plots: - if len(row) == 4: - k = int(row[k_ind]) - v = float(row[v_ind]) - reward = float(row[3]) - cost = -1.0 * reward - - if k not in list_costs: - list_costs[k] = OrderedDict() - - if v not in list_costs[k]: - list_costs[k][v] = [] - - list_costs[k][v].append(cost) - return list_costs - -def get_dict_baseline(fname, k_ind, v_ind): - list_costs = OrderedDict() - with open(fname, 'r') as csvfile: - plots = csv.reader(csvfile, delimiter=',') - next(plots, None) - for row in plots: - if len(row) == 4: - k = row[k_ind] - k = k.replace(" ", "") - if k == 'True': - k = 'Centralized' - else: - k = 'Decentralized' - - v = float(row[v_ind]) - reward = float(row[3]) - cost = -1.0 * reward - - if k not in list_costs: - list_costs[k] = OrderedDict() - - if v not in list_costs[k]: - list_costs[k][v] = [] - - list_costs[k][v].append(cost) - return list_costs - -def get_mean(list_costs): - # compute average over diff seeds for each parameter combo - avg_costs = OrderedDict() - for k in list_costs.keys(): - if k not in avg_costs: - avg_costs[k] = OrderedDict() - - for v in list_costs[k].keys(): - avg_costs[k][v] = np.mean(list_costs[k][v]) - return avg_costs - -def get_stddev(list_costs): - # compute standard deviation - std_costs = OrderedDict() - for k in list_costs.keys(): - if k not in std_costs: - std_costs[k] = OrderedDict() - - for v in list_costs[k].keys(): - std_costs[k][v] = np.std(list_costs[k][v]) - - return std_costs - -def get_max(list_costs): - # compute average over diff seeds for each parameter combo - max_val = -1.0 * np.Inf - - for k in list_costs.keys(): - for v in list_costs[k].keys(): - for i in list_costs[k][v]: - max_val = np.maximum(max_val, i) - return max_val - -baseline=True -fname_baseline = 'vel4_baseline.csv' -k_ind_baseline = 2 -v_ind_baseline = 0 - -fname = 'vel4.csv' -xlabel = 'Maximum Initial Velocity' - -k_ind = 1 -v_ind = 0 - -# baseline=True -# fname_baseline = 'vel_leader_ baseline.csv' -# k_ind_baseline = 2 -# v_ind_baseline = 0 - -# fname = 'vel_leader.csv' -# xlabel = 'Max Initial Velocity' -# ylabel = 'Avg Cost' -# k_ind = 1 -# v_ind = 0 - -# fname_baseline = 'rad4_baseline.csv' -# k_ind_baseline = 2 -# v_ind_baseline = 0 -# -# -# fname = 'rad4.csv' -# xlabel = 'Comm. Radius' -# k_ind = 0 -# v_ind = 2 - - -# fname = 'rad_leader.csv' -# xlabel = 'Comm. Radius' -# k_ind = 0 -# v_ind = 2 -# -# fname_baseline = 'rad_leader_baseline.csv' -# k_ind_baseline = 2 -# v_ind_baseline = 0 - - -# fname_baseline = 'n_baseline.csv' -# k_ind_baseline = 1 -# v_ind_baseline = 2 -# -# -# fname = 'n.csv' -# xlabel = 'Number of Agents' -# k_ind = 0 -# v_ind = 2 - - - - - -# -list_costs = get_dict(fname, k_ind, v_ind) -avg_costs = get_mean(list_costs) -std_costs = get_stddev(list_costs) - -max_val = get_max(list_costs) + 10.0 -ylabel = 'Average Cost' -title = ylabel + ' vs. ' + xlabel - -# plot -fig, ax = plt.subplots() -for k in avg_costs.keys(): - ax.errorbar(avg_costs[k].keys(), avg_costs[k].values(), yerr=std_costs[k].values(), marker='o', color=colors[str(k)], label='K=' + str(k)) - - -if baseline: - list_costs_baseline = get_dict_baseline(fname_baseline, k_ind_baseline, v_ind_baseline) - avg_costs_baseline = get_mean(list_costs_baseline) - std_costs_baseline = get_stddev(list_costs_baseline) - - for k in avg_costs_baseline.keys(): - ax.errorbar(avg_costs_baseline[k].keys(), avg_costs_baseline[k].values(), yerr=std_costs_baseline[k].values(), - marker='o', color=colors[str(k)], label=k) - - -ax.legend() -plt.title(title) -plt.ylim(top=max_val, bottom=0) - -plt.xlabel(xlabel) -plt.ylabel(ylabel) - -plt.savefig('vel.eps', format='eps') - -plt.show() - - diff --git a/results/old/rad.csv b/results/old/rad.csv deleted file mode 100644 index 8aaa47f..0000000 --- a/results/old/rad.csv +++ /dev/null @@ -1,25 +0,0 @@ -3.0, 1, 1, -38.194659380502856 -2.0, 1, 1, -43.08999690298034 -1.0, 1, 1, -111.43074906242308 -3.0, 2, 1, -38.644345561153266 -2.0, 2, 1, -40.85642811915737 -1.0, 2, 1, -61.91787179245059 -3.0, 3, 1, -38.390633266886304 -2.0, 3, 1, -39.780910746339686 -1.0, 3, 1, -49.95454129758206 -3.0, 4, 1, -38.43748580456146 -2.0, 4, 1, -39.92618615506887 -1.0, 4, 1, -49.65888279226742 -3.0, 1, 2, -36.091096429979714 -2.0, 1, 2, -41.22583217762869 -1.0, 1, 2, -120.10178556450725 -3.0, 2, 2, -36.25253483782206 -2.0, 2, 2, -39.536685306752446 -1.0, 2, 2, -61.82159278590088 -3.0, 3, 2, -36.1410397370208 -2.0, 3, 2, -37.66321896503462 -1.0, 3, 2, -53.499027657881 -3.0, 4, 2, -35.99840906800189 -2.0, 4, 2, -37.865297764016475 -1.0, 4, 2, -51.78525324723262 - diff --git a/results/old/rad2.csv b/results/old/rad2.csv deleted file mode 100644 index 8abb083..0000000 --- a/results/old/rad2.csv +++ /dev/null @@ -1,165 +0,0 @@ -1, 0, 3.0, -39.42192157627756 -1, 0, 2.0, -44.60039974810295 -1, 0, 1.5, -64.41286770673258 -1, 0, 1.0, -109.4878938720897 -1, 1, 3.0, -38.194659380502856 -1, 1, 2.0, -43.08999690298034 -1, 1, 1.5, -60.046980057190446 -1, 1, 1.0, -111.43074906242308 -1, 2, 3.0, -36.091096429979714 -1, 2, 2.0, -41.22583217762869 -1, 2, 1.5, -55.63933306554545 -1, 2, 1.0, -120.10178556450725 -1, 3, 3.0, -39.06325416705385 -1, 3, 2.0, -45.181720791085176 -1, 3, 1.5, -59.80218915037614 -1, 3, 1.0, -111.2680084637644 -1, 4, 3.0, -40.08361716912997 -1, 4, 2.0, -48.96310097208815 -1, 4, 1.5, -67.37786639506639 -1, 4, 1.0, -130.10677024824176 -1, 5, 3.0, -37.44254845145325 -1, 5, 2.0, -42.335113331383845 -1, 5, 1.5, -57.813961940443185 -1, 5, 1.0, -121.14747814342033 -1, 6, 3.0, -38.57179436285886 -1, 6, 2.0, -44.38019548621516 -1, 6, 1.5, -65.88806234423127 -1, 6, 1.0, -133.43607058701144 -1, 7, 3.0, -41.740099610843664 -1, 7, 2.0, -47.801768426682955 -1, 7, 1.5, -67.99931574038274 -1, 7, 1.0, -123.79238820950404 -1, 8, 3.0, -37.63359385888161 -1, 8, 2.0, -42.6001547687272 -1, 8, 1.5, -56.087140720453114 -1, 8, 1.0, -130.11099981900327 -1, 9, 3.0, -36.92166240227585 -1, 9, 2.0, -42.0184308060695 -1, 9, 1.5, -59.31411382988249 -1, 9, 1.0, -130.06149462894558 -2, 0, 3.0, -40.49683414112159 -2, 0, 2.0, -41.978656917486695 -2, 0, 1.5, -45.34965030114958 -2, 0, 1.0, -57.758516021453104 -2, 1, 3.0, -38.644345561153266 -2, 1, 2.0, -40.85642811915737 -2, 1, 1.5, -43.66469117410644 -2, 1, 1.0, -61.91787179245059 -2, 2, 3.0, -36.25253483782206 -2, 2, 2.0, -39.536685306752446 -2, 2, 1.5, -44.42883194936981 -2, 2, 1.0, -61.82159278590088 -2, 2, 3.0, -36.25253483782206 -2, 2, 2.0, -39.536685306752446 -2, 2, 1.5, -44.42883194936981 -2, 2, 1.0, -61.82159278590088 -2, 3, 3.0, -40.32349496624725 -2, 3, 2.0, -42.0323935755116 -2, 3, 1.5, -46.45446283630619 -2, 3, 1.0, -62.88191767136597 -2, 4, 3.0, -40.7679188991099 -2, 4, 2.0, -42.90799357566457 -2, 4, 1.5, -47.700370016215864 -2, 4, 1.0, -66.32504928830025 -2, 5, 3.0, -38.313067185312335 -2, 5, 2.0, -40.31861036376305 -2, 5, 1.5, -44.064442712509305 -2, 5, 1.0, -69.45765407434388 -2, 6, 3.0, -38.64529666630823 -2, 6, 2.0, -40.261402672954674 -2, 6, 1.5, -44.7966922494554 -2, 6, 1.0, -71.28889979744852 -2, 7, 3.0, -43.458025540853974 -2, 7, 2.0, -44.30720056838664 -2, 7, 1.5, -48.835284806220145 -2, 7, 1.0, -67.92356166888678 -2, 8, 3.0, -38.47587757440036 -2, 8, 2.0, -40.55043497689328 -2, 8, 1.5, -44.595790529226846 -2, 8, 1.0, -70.01132747974694 -2, 9, 3.0, -37.46389820455844 -2, 9, 2.0, -38.84190003828906 -2, 9, 1.5, -43.29818710074558 -2, 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-2.5, 2, True, -37.56312794728892 -2.5, 2, False, -3346.7637838042624 -2.5, 3, True, -37.298136572770865 -2.5, 3, False, -3349.432202062982 -2.5, 4, True, -39.30565498822652 -2.5, 4, False, -3412.88970757289 -2.5, 5, True, -39.33924576658575 -2.5, 5, False, -3407.818153877033 -2.5, 6, True, -38.966503158158645 -2.5, 6, False, -3423.1600211142163 -2.5, 7, True, -38.83645098706802 -2.5, 7, False, -3443.3894250917574 -2.5, 8, True, -35.072038568371184 -2.5, 8, False, -3292.480957019937 -2.5, 9, True, -37.552507851269944 -2.5, 9, False, -3270.56215738966 -3.5, 0, True, -50.964605169104075 -3.5, 0, False, -3910.5236665605926 -3.5, 1, True, -49.68562474225725 -3.5, 1, False, -3874.61851336474 -3.5, 2, True, -49.838420304205286 -3.5, 2, False, -3855.2215373651366 -3.5, 3, True, -47.89411596539312 -3.5, 3, False, -3888.027598991305 -3.5, 4, True, -50.948839838139946 -3.5, 4, False, -3926.5391498589133 -3.5, 5, True, -51.26282031181544 -3.5, 5, False, -3954.3298815503913 -3.5, 6, True, -52.049448111866994 -3.5, 6, False, -3985.6379555046237 -3.5, 7, True, -52.30341438534428 -3.5, 7, False, -3986.4916583481436 -3.5, 8, True, -46.203268521725825 -3.5, 8, False, -3814.4393211919405 -3.5, 9, True, -49.518465635656526 -3.5, 9, False, -3805.135241962176 diff --git a/results/plot_bargraph.py b/results/plot_bargraph.py deleted file mode 100644 index 8376714..0000000 --- a/results/plot_bargraph.py +++ /dev/null @@ -1,85 +0,0 @@ -import matplotlib -import matplotlib.pyplot as plt -import csv -import numpy as np -from collections import OrderedDict - -font = {'family': 'serif', - 'weight': 'bold', - 'size': 12} -matplotlib.rc('font', **font) - -_CENTRALIZED = 'Centr.' -_DECENTRALIZED = 'Decentr.' - -def main(): - - # fig_fname = 'airsim_trained' - - fig_fname = 'airsim_test' - - - save_dir = 'fig/' - - k_ind = 0 - - mean_cost_cent = [2.041724997601464] - std_cost_cent = [0.07619242768446846] - - mean_cost_decent = [9.335155311869324] - std_cost_decent = [2.6593089180772074] - - - mean_costs_airsim, std_costs_airsim = get_dict(['airsim_trained2.csv'], k_ind) - mean_costs_stoch, std_costs_stoch = get_dict(['stoch_transfer_to_airsim2.csv'], k_ind) - ylabel = 'Cost' - - # plot - fig, ax = plt.subplots() - - ind = np.array(range(1,5)) - width = 0.35/2 # the width of the bars - # p1 = ax.bar(ind, menMeans, width, bottom=0 * cm, yerr=menStd) - # p2 = ax.bar(ind + width, womenMeans, width, bottom=0 * cm, yerr=womenStd) - - p1 = ax.bar(ind-width, mean_costs_airsim.values(), width=width*2, yerr = std_costs_airsim.values()) - p2 = ax.bar(ind+width, mean_costs_stoch.values(), width=width*2, yerr = std_costs_stoch.values()) - p3 = ax.bar(0, mean_cost_cent, width=width*3, yerr=std_cost_cent) - p4 = ax.bar(-1, mean_cost_decent, width=width*3, yerr=std_cost_decent) - - - ax.legend((p1[0], p2[0], p3[0], p4[0]), ('Trained: AirSim', 'Trained: Point-Masses', 'Global', 'Local')) - plt.title('Testing in AirSim') - # plt.ylim(top=max_val, bottom=0) - plt.xlabel('K') - plt.ylabel(ylabel) - - ax.set_xticklabels(('', '', '', '1', '2', '3', '4')) - - plt.savefig(save_dir + fig_fname + '.eps', format='eps') - plt.show() - - -def get_dict(fnames, k_ind): - mean_costs = OrderedDict() - std_costs = OrderedDict() - - for fname in fnames: - with open(fname, 'r') as csvfile: - plots = csv.reader(csvfile, delimiter=',') - next(plots, None) - for row in plots: - - if len(row) == 3: - k = row[k_ind].strip() - mean = float(row[1]) * -1.0 - std = float(row[2]) - - mean_costs[k] = mean - std_costs[k] = std - - return mean_costs, std_costs - - -if __name__ == "__main__": - main() diff --git a/results/plot_nn.py b/results/plot_nn.py deleted file mode 100644 index bb43f91..0000000 --- a/results/plot_nn.py +++ /dev/null @@ -1,107 +0,0 @@ -import matplotlib -import matplotlib.pyplot as plt -import csv -import numpy as np -from collections import OrderedDict - -font = {'family': 'serif', - 'weight': 'bold', - 'size': 14} -matplotlib.rc('font', **font) - -_CENTRALIZED = 'Global' -_DECENTRALIZED = 'Local' - -def main(): - - fig_fname = 'hidden_size' - - fnames = ['hidden_size.csv'] - xlabel = 'Num. Neurons per Layer' - k_ind = 0 - v_ind = 1 - arrow_params = None - - colors = {_CENTRALIZED: 'green', _DECENTRALIZED: 'red', '4': 'blue', '3': 'darkviolet', '2': 'orange', '1': 'gold'} - save_dir = 'fig/' - - mean_costs, std_costs = get_dict(fnames, k_ind, v_ind) - - max_val, min_dec = get_max(mean_costs) - max_val = max_val + 10.0 - ylabel = 'Cost' - title = 'Cost vs. GNN Architecture' - - # plot - fig, ax = plt.subplots() - for k in mean_costs.keys(): - # if k != '4': - if not (k == _CENTRALIZED or k == _DECENTRALIZED): - label = k + ' Hidden Layers' - else: - label = k - ax.errorbar(mean_costs[k].keys(), mean_costs[k].values(), yerr=std_costs[k].values(), marker='o', color=colors[k], - label=label) - - ax.legend() - plt.title(title) - plt.ylim(top=300, bottom=0) - plt.xlabel(xlabel) - plt.ylabel(ylabel) - - if max_val < min_dec < np.Inf and arrow_params: - min_dec = int(np.floor(min_dec / 100.0)*100) - # plt.arrow(x=3.3, y=400.0, dx=0.0, dy=30.0, color='r', width=0.03, head_length=30) - plt.arrow(**arrow_params) - plt.text(x=text_params['x'], y=text_params['y'], s='Cost > '+str(min_dec), color='r') - - plt.savefig(save_dir + fig_fname + '.eps', format='eps') - plt.show() - - -def get_dict(fnames, k_ind, v_ind): - mean_costs = OrderedDict() - std_costs = OrderedDict() - - for fname in fnames: - with open(fname, 'r') as csvfile: - plots = csv.reader(csvfile, delimiter=',') - next(plots, None) - for row in plots: - - if True: # len(row) == 4: - k = row[k_ind].strip() - - if k == 'True': - k = _CENTRALIZED - elif k == 'False': - k = _DECENTRALIZED - - v = float(row[v_ind]) - - mean = float(row[2]) * -1.0 - std = float(row[3]) - if k not in mean_costs: - mean_costs[k] = OrderedDict() - std_costs[k] = OrderedDict() - mean_costs[k][v] = mean - std_costs[k][v] = std - - return mean_costs, std_costs - - -def get_max(list_costs): - # compute average over diff seeds for each parameter combo - max_val = -1.0 * np.Inf - min_decentralized = 1.0 * np.Inf - - for k in list_costs.keys(): - for v in list_costs[k].keys(): - if k != _DECENTRALIZED: - max_val = np.maximum(max_val, list_costs[k][v]) - else: - min_decentralized = np.minimum(min_decentralized, list_costs[k][v]) - return max_val, min_decentralized - -if __name__ == "__main__": - main() diff --git a/results/plot_results.py b/results/plot_results.py deleted file mode 100644 index a4b7b25..0000000 --- a/results/plot_results.py +++ /dev/null @@ -1,168 +0,0 @@ -import matplotlib -import matplotlib.pyplot as plt -import csv -import numpy as np -from collections import OrderedDict - -font = {'family': 'serif', - 'weight': 'bold', - 'size': 14} -matplotlib.rc('font', **font) - -_CENTRALIZED = 'Global' -_DECENTRALIZED = 'Local' - -def main(): - - fig_fname = 'n' - - if fig_fname == 'transfer_grid_n': - fnames = ['transfer_grid_n.csv'] - xlabel = 'Number of Agents' - k_ind = 0 - v_ind = 1 - arrow_params = None - - if fig_fname == 'transfer_twoflocks_n': - fnames = ['transfer_twoflocks_n.csv'] - xlabel = 'Number of Agents' - k_ind = 0 - v_ind = 1 - arrow_params = None - - if fig_fname == 'vel': - fnames = ['vel.csv', 'vel_baseline.csv'] - xlabel = 'Maximum Initial Velocity' - k_ind = 1 - v_ind = 0 - - arrow_params = {'x':3.3, 'y': 400.0, 'dx':0.0, 'dy':30.0, 'width':0.03, 'head_length':30, 'color':'r'} - text_params = {'x': 2.7, 'y': 370} - - if fig_fname == 'transfer_vel': - fnames = ['transfer_vel.csv', 'vel_baseline.csv'] - xlabel = 'Maximum Initial Velocity' - k_ind = 1 - v_ind = 0 - - arrow_params = None - - # arrow_params = {'x':70, 'y': 400.0, 'dx':0.0, 'dy':30.0, 'width':1.5, 'head_length':30, 'color':'r'} - # text_params = {'x': 55, 'y': 370} - - if fig_fname == 'transfer_n': - fnames = ['transfer_n.csv', 'n_baseline.csv'] - xlabel = 'Number of Agents' - k_ind = 0 - v_ind = 1 - - arrow_params = {'x':105, 'y': 230.0, 'dx':0.0, 'dy':25.0, 'width':1.5, 'head_length':30, 'color':'r'} - text_params = {'x': 85, 'y': 200} - - if fig_fname == 'transfer_leader_vel': - fnames = ['transfer_leader_vel.csv', 'vel_leader_baseline.csv'] - xlabel = 'Maximum Initial Velocity' - k_ind = 1 - v_ind = 0 - arrow_params = None - - if fig_fname == 'rad': - fnames = ['rad.csv', 'rad_baseline.csv'] - xlabel = 'Comm. Radius' - k_ind = 0 - v_ind = 1 - - arrow_params = None - - if fig_fname == 'n': - fnames = ['n.csv', 'n_baseline.csv'] - xlabel = 'Number of Agents' - k_ind = 0 - v_ind = 1 - - arrow_params = {'x':85, 'y': 300.0, 'dx':0.0, 'dy':30.0, 'width':1.5, 'head_length':30, 'color':'r'} - text_params = {'x': 70, 'y': 370} - - colors = {_CENTRALIZED: 'green', _DECENTRALIZED: 'red', '4': 'blue', '3': 'darkviolet', '2': 'orange', '1': 'gold'} - save_dir = 'fig/' - - mean_costs, std_costs = get_dict(fnames, k_ind, v_ind) - - max_val, min_dec = get_max(mean_costs) - max_val = max_val + 10.0 - ylabel = 'Cost' - title = ylabel + ' vs. ' + xlabel - - # plot - fig, ax = plt.subplots() - for k in mean_costs.keys(): - # if k != '4': - if not (k == _CENTRALIZED or k == _DECENTRALIZED): - label = 'K=' + k - else: - label = k - ax.errorbar(mean_costs[k].keys(), mean_costs[k].values(), yerr=std_costs[k].values(), marker='o', color=colors[k], - label=label) - - ax.legend() - plt.title(title) - plt.ylim(top=max_val, bottom=0) - plt.xlabel(xlabel) - plt.ylabel(ylabel) - - if max_val < min_dec < np.Inf and arrow_params: - min_dec = int(np.floor(min_dec / 100.0)*100) - # plt.arrow(x=3.3, y=400.0, dx=0.0, dy=30.0, color='r', width=0.03, head_length=30) - plt.arrow(**arrow_params) - plt.text(x=text_params['x'], y=text_params['y'], s='Cost > '+str(min_dec), color='r') - - plt.savefig(save_dir + fig_fname + '.eps', format='eps') - plt.show() - - -def get_dict(fnames, k_ind, v_ind): - mean_costs = OrderedDict() - std_costs = OrderedDict() - - for fname in fnames: - with open(fname, 'r') as csvfile: - plots = csv.reader(csvfile, delimiter=',') - next(plots, None) - for row in plots: - - if True: # len(row) == 4: - k = row[k_ind].strip() - - if k == 'True': - k = _CENTRALIZED - elif k == 'False': - k = _DECENTRALIZED - - v = float(row[v_ind]) - - mean = float(row[2]) * -1.0 - std = float(row[3]) - if k not in mean_costs: - mean_costs[k] = OrderedDict() - std_costs[k] = OrderedDict() - mean_costs[k][v] = mean - std_costs[k][v] = std - - return mean_costs, std_costs - - -def get_max(list_costs): - # compute average over diff seeds for each parameter combo - max_val = -1.0 * np.Inf - min_decentralized = 1.0 * np.Inf - - for k in list_costs.keys(): - for v in list_costs[k].keys(): - if k != _DECENTRALIZED: - max_val = np.maximum(max_val, list_costs[k][v]) - else: - min_decentralized = np.minimum(min_decentralized, list_costs[k][v]) - return max_val, min_decentralized - -if __name__ == "__main__": - main() diff --git a/results/rad.csv b/results/rad.csv deleted file mode 100644 index 94de317..0000000 --- a/results/rad.csv +++ /dev/null @@ -1,29 +0,0 @@ -k, comm_radius, reward -4, 4.0, -28.604341184720358, 2.785009069425716 -3, 4.0, -29.258205964235696, 2.8039124204459136 -2, 4.0, -28.954362643950894, 2.670866914406372 -1, 4.0, -31.606325170505045, 3.0262155929783408 -1, 3.0, -31.69065203280814, 3.435764166655853 -1, 2.5, -30.9645269384228, 3.179185440574518 -1, 2.0, -31.74263258329243, 3.226175205490666 -1, 1.5, -58.604169195150256, 20.431188919321954 -1, 1.0, -229.15979292372407, 122.16289677324487 -2, 3.0, -29.351584659516618, 2.7766175024213786 -2, 2.5, -29.133294294004866, 2.696532059169444 -2, 2.0, -30.30331212166963, 2.7409862324263674 -2, 1.5, -33.06493390855818, 2.936033937624822 -2, 1.0, -50.51020781200708, 12.566522373659023 -3, 3.0, -29.33382391453943, 2.8138072342757634 -3, 2.5, -29.23315525970899, 2.675785982848032 -3, 2.0, -29.8696894261329, 2.738740138936384 -3, 1.5, -31.26884552979589, 2.697956516540482 -3, 1.0, -40.95792156471694, 4.1560902247655385 -4, 3.0, -28.797047794471514, 2.6790574740301927 -4, 2.5, -28.44558367398712, 2.569181273123399 -4, 2.0, -28.999198864859647, 2.5661520530563546 -4, 1.5, -29.983003877649498, 2.6451828889869327 -4, 1.0, -38.293215563303235, 3.1199301223969256 -4, 4.0, -34.152806533996355, 4.115580451721444 -3, 4.0, -34.7288654153865, 4.172716013152299 -2, 4.0, -34.69124666223094, 4.1762191630953955 -1, 4.0, -37.19132506809642, 4.3176471922570165 \ No newline at end of file diff --git a/results/rad_baseline.csv b/results/rad_baseline.csv deleted file mode 100644 index cce5ad7..0000000 --- a/results/rad_baseline.csv +++ /dev/null @@ -1,13 +0,0 @@ -comm_radius, centralized, reward -True, 4.0, -13.9834284252706, 1.8111342628863647 -False, 4.0, -17.90172953618245, 2.4269465985733905 -True, 3.0, -14.024036847128665, 1.786227064984601 -False, 3.0, -27.300532671593267, 3.437283584463457 -True, 2.5, -14.255542008190947, 1.776702098164838 -False, 2.5, -43.0064103662455, 4.433323210617722 -True, 2.0, -14.538311983554106, 1.7718423038592899 -False, 2.0, -102.62622761650303, 7.421507797923986 -True, 1.5, -14.810441187316304, 1.7669888441661243 -False, 1.5, -1055.212676867627, 136.01985599791774 -True, 1.0, -14.899697200560103, 1.8157149806086754 -False, 1.0, -6535.8762397044575, 554.0006754735136 diff --git a/results/stoch_transfer_to_airsim.csv b/results/stoch_transfer_to_airsim.csv deleted file mode 100644 index 06dd444..0000000 --- a/results/stoch_transfer_to_airsim.csv +++ /dev/null @@ -1,5 +0,0 @@ -k, reward, std -1, -5.905066259211232, 2.2105622358605097 -2, -3.9234482654695833, 1.043596249170607 -3, -6.772500496052969, 2.5845733981345425 -4, -7.013095494140333, 2.6972839846893635 diff --git a/results/stoch_transfer_to_airsim2.csv b/results/stoch_transfer_to_airsim2.csv deleted file mode 100644 index b40afcc..0000000 --- a/results/stoch_transfer_to_airsim2.csv +++ /dev/null @@ -1,6 +0,0 @@ -k, reward, std -1, -5.371718155747085, 2.107049489042026 -2, -3.8907236770070335, 1.9490529683049205 -3, -3.5587343557409077, 2.3253053480811885 -4, -4.410319276747424, 2.4066994410746116 - diff --git a/results/transfer_grid_n.csv b/results/transfer_grid_n.csv deleted file mode 100644 index 08e3726..0000000 --- a/results/transfer_grid_n.csv +++ /dev/null @@ -1,21 +0,0 @@ -k, n_agents, reward -1, 50, -319.80587027649506, 5.684341886080802e-14 -1, 100, -492.12022704774455, 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a/results/transfer_leader_n.csv b/results/transfer_leader_n.csv deleted file mode 100644 index 82c8210..0000000 --- a/results/transfer_leader_n.csv +++ /dev/null @@ -1,21 +0,0 @@ -k, n_agents, reward -1, 50, -515.9994524147301, 313.5784168250287 -1, 100, -339.18597907859805, 270.6093256284251 -1, 150, -373.9424555273811, 258.21659520982405 -1, 200, -324.27758713439187, 149.89792028439558 -1, 250, -303.7102669285242, 164.77666566978914 -2, 50, -432.67723292200264, 295.19232537999255 -2, 100, -250.5246206481408, 259.75816841720393 -2, 150, -293.4102778814613, 198.0793779562355 -2, 200, -229.96628904615636, 105.04130817128582 -2, 250, -224.24280596005173, 102.92725510549074 -3, 50, -261.53031903174156, 186.02064288640778 -3, 100, -167.28968407370834, 216.3604627074367 -3, 150, -167.02956962505803, 83.76812642239042 -3, 200, -139.0463238534638, 47.62971905724153 -3, 250, -147.61418367651018, 53.20762942603185 -4, 50, -210.53939437320545, 149.4274268403402 -4, 100, -151.29911558786094, 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a/results/transfer_twoflocks_n.csv +++ /dev/null @@ -1,21 +0,0 @@ -k, n_agents, reward -1, 50, -319.80587027649506, 5.684341886080802e-14 -1, 100, -492.12022704774455, 6.844854043943256e-14 -1, 150, -915.4057635411979, 1.7993604792120223e-08 -1, 200, -1590.0710760021725, 2.0568840543061577e-07 -1, 250, -2609.8944611084817, 0.20187853116588028 -2, 50, -151.2956686633841, 2.6961092324792364e-07 -2, 100, -260.1699197834481, 2.521220901081242e-07 -2, 150, -570.5130459651994, 4.545833307030627e-07 -2, 200, -1133.8038371198654, 3.719707407434866e-06 -2, 250, -1920.4878030054829, 0.000591295858710477 -3, 50, -101.26147432710142, 9.043138199738023e-08 -3, 100, -161.86534917630868, 3.121021354971091e-07 -3, 150, -391.5303883168649, 1.0187779902629701e-06 -3, 200, -840.0998688888042, 0.0006903486601959455 -3, 250, -1521.415298755905, 5.504759090737461e-06 -4, 50, -103.59011014668968, 5.788794237175581e-07 -4, 100, -159.99725560778944, 4.225180365636876e-07 -4, 150, -377.7787583962748, 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