From a7cb94ebb383c09c90b0d21c6a618bce5b38b02b Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Tue, 2 Jul 2019 15:36:58 -0400
Subject: [PATCH] added new special maps
---
torch_training/railway/complex_scene_2.pkl | Bin 0 -> 1916 bytes
torch_training/railway/complex_scene_3.pkl | Bin 0 -> 1837 bytes
torch_training/railway/flatland.pkl | Bin 0 -> 2684 bytes
torch_training/training_navigation.py | 9 +++++----
4 files changed, 5 insertions(+), 4 deletions(-)
create mode 100644 torch_training/railway/complex_scene_2.pkl
create mode 100644 torch_training/railway/complex_scene_3.pkl
create mode 100644 torch_training/railway/flatland.pkl
diff --git a/torch_training/railway/complex_scene_2.pkl b/torch_training/railway/complex_scene_2.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..5ebb8ab1715b3c9f01171116ac519128f4d67234
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diff --git a/torch_training/railway/complex_scene_3.pkl b/torch_training/railway/complex_scene_3.pkl
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diff --git a/torch_training/railway/flatland.pkl b/torch_training/railway/flatland.pkl
new file mode 100644
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diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index de4b792..d085a8b 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -43,7 +43,7 @@ env = RailEnv(width=15,
"""
env = RailEnv(width=10,
height=20, obs_builder_object=TreeObsForRailEnv(max_depth=2, predictor=ShortestPathPredictorForRailEnv()))
-env.load("./railway/complex_scene.pkl")
+env.load("./railway/flatland.pkl")
file_load = True
"""
@@ -79,7 +79,7 @@ agent = Agent(state_size, action_size, "FC", 0)
agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint15000.pth'))
demo = True
-record_images = False
+record_images = True
def max_lt(seq, val):
"""
@@ -140,6 +140,7 @@ for trials in range(1, n_trials + 1):
final_obs = obs.copy()
final_obs_next = obs.copy()
for a in range(env.get_num_agents()):
+ print(a)
data, distance, agent_data = env.obs_builder.split_tree(tree=np.array(obs[a]), num_features_per_node=8,
current_depth=0)
data = norm_obs_clip(data)
@@ -164,14 +165,14 @@ for trials in range(1, n_trials + 1):
if demo:
env_renderer.renderEnv(show=True, show_observations=False)
if record_images:
- env_renderer.gl.saveImage("./Images/frame_{:04d}.bmp".format(step))
+ 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 = 2 #agent.act(agent_obs[a], eps=eps)
action_prob[action] += 1
action_dict.update({a: action})
# Environment step
--
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