From 2cf1b9d133b08e9b2e963c545250332cf1f262ec Mon Sep 17 00:00:00 2001
From: u214892 <u214892@sbb.ch>
Date: Wed, 10 Jul 2019 16:43:24 +0200
Subject: [PATCH] #42 run baselines in ci
---
torch_training/bla.py | 204 +++++++++++++++++++++---------------------
1 file changed, 102 insertions(+), 102 deletions(-)
diff --git a/torch_training/bla.py b/torch_training/bla.py
index 5ae7b51..37d9a09 100644
--- a/torch_training/bla.py
+++ b/torch_training/bla.py
@@ -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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