From f4e7017f770f8245747323b90a4f5dcf55600177 Mon Sep 17 00:00:00 2001 From: Erik Nygren <erik.nygren@sbb.ch> Date: Mon, 8 Jul 2019 09:50:44 -0400 Subject: [PATCH] simplified training example for participants --- parameters.txt | 2 +- score_test.py | 6 +- torch_training/Nets/avoid_checkpoint15000.pth | Bin 525023 -> 1704675 bytes torch_training/multi_agent_training.py | 194 ++++++++++++++++++ torch_training/training_navigation.py | 10 +- utils/misc_utils.py | 8 +- 6 files changed, 205 insertions(+), 15 deletions(-) create mode 100644 torch_training/multi_agent_training.py diff --git a/parameters.txt b/parameters.txt index 80ad8c2..173c5a7 100644 --- a/parameters.txt +++ b/parameters.txt @@ -1,4 +1,4 @@ -{'Test_0':[100,100,5,3], +{'Test_0':[20,20,20,3], 'Test_1':[10,10,3,4321], 'Test_2':[10,10,5,123], 'Test_3':[50,50,5,21], diff --git a/score_test.py b/score_test.py index bf309d2..ff4a94c 100644 --- a/score_test.py +++ b/score_test.py @@ -1,9 +1,7 @@ import time import numpy as np -import torch -from torch_training.dueling_double_dqn import Agent from utils.misc_utils import RandomAgent, run_test with open('parameters.txt','r') as inf: @@ -23,8 +21,8 @@ test_results = [] test_times = [] test_dones = [] # Load agent -agent = Agent(state_size, action_size, "FC", 0) -agent.qnetwork_local.load_state_dict(torch.load('./torch_training/Nets/avoid_checkpoint1700.pth')) +# agent = Agent(state_size, action_size, "FC", 0) +# agent.qnetwork_local.load_state_dict(torch.load('./torch_training/Nets/avoid_checkpoint1700.pth')) agent = RandomAgent(state_size, action_size) start_time_scoring = time.time() test_idx = 0 diff --git a/torch_training/Nets/avoid_checkpoint15000.pth b/torch_training/Nets/avoid_checkpoint15000.pth index ba488f1a074af3f62ad87d54f61a251e8292ae50..833de82752968a507b8e5397e76f55c26558d946 100644 GIT binary patch literal 1704675 zcmZ^~2T+vF@;!_qA|Mh3MS>zp1O)^|;F(?!5eZ5(fG8OOMQ{;OM39^_h~%W?91O72 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zLY7auCdvA*X@+OJD5h*rLW3<7Pg=;cn`_7OaV~SQ@{2et{$)Mpnh8z9(-qWeRtlJJ zD*;V$8CH6w5gLVDhWB=d>9wbxIHtoHI*O-a`=K$c6MDgVofd5TIfB<stD&X;Rig!6 ziAvXJ;Ms<e{1x2`=&bh^w0_6n;dLo=K%Yg^vf->}X&+sxuEE~Dyo$!=3LcT#=V*|7 GgZ@9B{Yw-8 diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py new file mode 100644 index 0000000..eb11fb1 --- /dev/null +++ b/torch_training/multi_agent_training.py @@ -0,0 +1,194 @@ +import random +from collections import deque + +import matplotlib.pyplot as plt +import numpy as np +import torch +from dueling_double_dqn import Agent +from flatland.envs.generators import complex_rail_generator +from flatland.envs.observations import TreeObsForRailEnv +from flatland.envs.predictions import ShortestPathPredictorForRailEnv +from flatland.envs.rail_env import RailEnv +from flatland.utils.rendertools import RenderTool + +from utils.observation_utils import norm_obs_clip, split_tree + +random.seed(1) +np.random.seed(1) + +""" +env = RailEnv(width=10, + height=20, obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())) +env.load("./railway/complex_scene.pkl") +file_load = True +""" + +x_dim = np.random.randint(8, 20) +y_dim = np.random.randint(8, 20) +n_agents = np.random.randint(3, 8) +n_goals = n_agents + np.random.randint(0, 3) +min_dist = int(0.75 * min(x_dim, y_dim)) +env = RailEnv(width=x_dim, + height=y_dim, + rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist, + max_dist=99999, + seed=0), + obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()), + number_of_agents=n_agents) +env.reset(True, True) +file_load = False +""" + +""" +observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()) +env_renderer = RenderTool(env, gl="PILSVG", ) +handle = env.get_agent_handles() +features_per_node = 9 +state_size = features_per_node * 85 * 2 +action_size = 5 +n_trials = 30000 +max_steps = int(3 * (env.height + env.width)) +eps = 1. +eps_end = 0.005 +eps_decay = 0.9995 +action_dict = dict() +final_action_dict = dict() +scores_window = deque(maxlen=100) +done_window = deque(maxlen=100) +time_obs = deque(maxlen=2) +scores = [] +dones_list = [] +action_prob = [0] * action_size +agent_obs = [None] * env.get_num_agents() +agent_next_obs = [None] * env.get_num_agents() +agent = Agent(state_size, action_size, "FC", 0) +agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint30000.pth')) + +demo = True +record_images = False + +for trials in range(1, n_trials + 1): + + if trials % 50 == 0 and not demo: + x_dim = np.random.randint(8, 20) + y_dim = np.random.randint(8, 20) + n_agents = np.random.randint(3, 8) + n_goals = n_agents + np.random.randint(0, 3) + min_dist = int(0.75 * min(x_dim, y_dim)) + env = RailEnv(width=x_dim, + height=y_dim, + rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist, + max_dist=99999, + seed=0), + obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()), + number_of_agents=n_agents) + env.reset(True, True) + max_steps = int(3 * (env.height + env.width)) + agent_obs = [None] * env.get_num_agents() + agent_next_obs = [None] * env.get_num_agents() + # Reset environment + if file_load: + obs = env.reset(False, False) + else: + obs = env.reset(True, True) + if demo: + env_renderer.set_new_rail() + obs_original = obs.copy() + final_obs = obs.copy() + final_obs_next = obs.copy() + for a in range(env.get_num_agents()): + data, distance, agent_data = split_tree(tree=np.array(obs[a]), num_features_per_node=features_per_node, + current_depth=0) + data = norm_obs_clip(data) + distance = norm_obs_clip(distance) + agent_data = np.clip(agent_data, -1, 1) + obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data)) + agent_data = env.agents[a] + speed = 1 # np.random.randint(1,5) + agent_data.speed_data['speed'] = 1. / speed + + for i in range(2): + time_obs.append(obs) + # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5) + for a in range(env.get_num_agents()): + agent_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a])) + + score = 0 + env_done = 0 + # Run episode + for step in range(max_steps): + if demo: + env_renderer.renderEnv(show=True, show_observations=True) + # observation_helper.util_print_obs_subtree(obs_original[0]) + if record_images: + env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(step)) + # print(step) + # Action + for a in range(env.get_num_agents()): + if demo: + eps = 0 + # action = agent.act(np.array(obs[a]), eps=eps) + action = agent.act(agent_obs[a], eps=eps) + action_prob[action] += 1 + action_dict.update({a: action}) + # Environment step + + next_obs, all_rewards, done, _ = env.step(action_dict) + # print(all_rewards,action) + obs_original = next_obs.copy() + for a in range(env.get_num_agents()): + data, distance, agent_data = split_tree(tree=np.array(next_obs[a]), num_features_per_node=features_per_node, + current_depth=0) + data = norm_obs_clip(data) + distance = norm_obs_clip(distance) + agent_data = np.clip(agent_data, -1, 1) + next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data)) + time_obs.append(next_obs) + + # Update replay buffer and train agent + for a in range(env.get_num_agents()): + agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a])) + if done[a]: + final_obs[a] = agent_obs[a].copy() + final_obs_next[a] = agent_next_obs[a].copy() + final_action_dict.update({a: action_dict[a]}) + if not demo and not done[a]: + agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a]) + score += all_rewards[a] / env.get_num_agents() + + agent_obs = agent_next_obs.copy() + if done['__all__']: + env_done = 1 + for a in range(env.get_num_agents()): + agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a]) + break + # Epsilon decay + eps = max(eps_end, eps_decay * eps) # decrease epsilon + + done_window.append(env_done) + scores_window.append(score / max_steps) # save most recent score + scores.append(np.mean(scores_window)) + dones_list.append((np.mean(done_window))) + + print( + '\rTraining {} Agents on ({},{}).\t Episode {}\t Average Score: {:.3f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format( + env.get_num_agents(), x_dim, y_dim, + trials, + np.mean(scores_window), + 100 * np.mean(done_window), + eps, action_prob / np.sum(action_prob)), end=" ") + + if trials % 100 == 0: + print( + '\rTraining {} Agents.\t Episode {}\t Average Score: {:.3f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format( + env.get_num_agents(), + trials, + np.mean(scores_window), + 100 * np.mean(done_window), + eps, + action_prob / np.sum(action_prob))) + torch.save(agent.qnetwork_local.state_dict(), + './Nets/avoid_checkpoint' + str(trials) + '.pth') + action_prob = [1] * action_size +plt.plot(scores) +plt.show() diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py index 0356b53..3007ce5 100644 --- a/torch_training/training_navigation.py +++ b/torch_training/training_navigation.py @@ -44,7 +44,7 @@ env = RailEnv(width=15, env = RailEnv(width=10, - height=20, obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv())) + height=20, obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())) env.load("./railway/complex_scene.pkl") file_load = True """ @@ -62,8 +62,8 @@ env = RailEnv(width=x_dim, number_of_agents=n_agents) env.reset(True, True) file_load = False - """ + """ observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()) env_renderer = RenderTool(env, gl="PILSVG",) @@ -87,9 +87,9 @@ action_prob = [0] * action_size agent_obs = [None] * env.get_num_agents() agent_next_obs = [None] * env.get_num_agents() agent = Agent(state_size, action_size, "FC", 0) -# agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pth')) +agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint30000.pth')) -demo = False +demo = True record_images = False @@ -146,7 +146,7 @@ for trials in range(1, n_trials + 1): for step in range(max_steps): if demo: env_renderer.renderEnv(show=True, show_observations=True) - observation_helper.util_print_obs_subtree(obs_original[0]) + # observation_helper.util_print_obs_subtree(obs_original[0]) if record_images: env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(step)) # print(step) diff --git a/utils/misc_utils.py b/utils/misc_utils.py index d4c6ef8..03c9fdd 100644 --- a/utils/misc_utils.py +++ b/utils/misc_utils.py @@ -4,8 +4,7 @@ from collections import deque import numpy as np from flatland.envs.generators import complex_rail_generator -from flatland.envs.observations import TreeObsForRailEnv -from flatland.envs.predictions import ShortestPathPredictorForRailEnv +from flatland.envs.observations import GlobalObsForRailEnv from flatland.envs.rail_env import RailEnv from line_profiler import LineProfiler @@ -69,7 +68,7 @@ def run_test(parameters, agent, test_nr=0, tree_depth=3): features_per_node = 9 start_time_scoring = time.time() action_dict = dict() - nr_trials_per_test = 100 + nr_trials_per_test = 5 print('Running Test {} with (x_dim,y_dim) = ({},{}) and {} Agents.'.format(test_nr, parameters[0], parameters[1], parameters[2])) @@ -88,8 +87,7 @@ def run_test(parameters, agent, test_nr=0, tree_depth=3): rail_generator=complex_rail_generator(nr_start_goal=nr_paths, nr_extra=5, min_dist=min_dist, max_dist=99999, seed=parameters[3]), - obs_builder_object=TreeObsForRailEnv(max_depth=tree_depth, - predictor=ShortestPathPredictorForRailEnv()), + obs_builder_object=GlobalObsForRailEnv(), number_of_agents=parameters[2]) max_steps = int(3 * (env.height + env.width)) lp_step = lp(env.step) -- GitLab