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Commit 2cf1b9d1 authored by u214892's avatar u214892
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#42 run baselines in ci

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torch_training/bla.py
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......@@ -92,108 +92,108 @@ def main(argv):
print("Going to run training for {} trials...".format(n_trials))
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]),
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=False)
# observation_helper.util_print_obs_subtree(obs_original[0])
if record_images:
env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
frame_step += 1
# 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]),
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)))
# 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]),
# 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=False)
# # observation_helper.util_print_obs_subtree(obs_original[0])
# if record_images:
# env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
# frame_step += 1
# # 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]),
# 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(
......
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