From 6980b3908717d64535530937ba9e1b1639ca9546 Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Tue, 23 Apr 2019 10:45:40 +0200
Subject: [PATCH] code cleanup
---
examples/training_navigation.py | 41 ++++++++++++++++-----------------
1 file changed, 20 insertions(+), 21 deletions(-)
diff --git a/examples/training_navigation.py b/examples/training_navigation.py
index b78851c..2071a5d 100644
--- a/examples/training_navigation.py
+++ b/examples/training_navigation.py
@@ -3,12 +3,11 @@ from flatland.core.env_observation_builder import TreeObsForRailEnv
from flatland.utils.rendertools import *
from flatland.baselines.dueling_double_dqn import Agent
from collections import deque
-import torch,random
+import torch, random
random.seed(1)
np.random.seed(1)
-
# Example generate a rail given a manual specification,
# a map of tuples (cell_type, rotation)
transition_probability = [1.0, # empty cell - Case 0
@@ -48,13 +47,12 @@ for trials in range(1, n_trials + 1):
obs = env.reset()
# env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
-
score = 0
env_done = 0
# Run episode
for step in range(100):
- if trials >= 114:
+ if trials > 114:
env_renderer.renderEnv(show=True)
# Action
@@ -63,9 +61,7 @@ for trials in range(1, n_trials + 1):
action_dict.update({a: action})
# Environment step
- print(trials,step)
next_obs, all_rewards, done, _ = env.step(action_dict)
- print("stepped")
# Update replay buffer and train agent
for a in range(env.number_of_agents):
@@ -85,21 +81,24 @@ for trials in range(1, n_trials + 1):
scores.append(np.mean(scores_window))
dones_list.append((np.mean(done_window)))
- print('\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f}'.format(env.number_of_agents,
- trials,
- np.mean(
- scores_window),
- 100 * np.mean(
- done_window),
- eps),
+ print('\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f}'.format(
+ env.number_of_agents,
+ trials,
+ np.mean(
+ scores_window),
+ 100 * np.mean(
+ done_window),
+ eps),
end=" ")
if trials % 100 == 0:
print(
- '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f}'.format(env.number_of_agents,
- trials,
- np.mean(
- scores_window),
- 100 * np.mean(
- done_window),
- eps))
- torch.save(agent.qnetwork_local.state_dict(), '../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
+ '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f}'.format(
+ env.number_of_agents,
+ trials,
+ np.mean(
+ scores_window),
+ 100 * np.mean(
+ done_window),
+ eps))
+ torch.save(agent.qnetwork_local.state_dict(),
+ '../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
--
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