From b639031b93b702fa0892162711b0d0d9258a83e3 Mon Sep 17 00:00:00 2001
From: u214892 <u214892@sbb.ch>
Date: Thu, 6 Jun 2019 11:41:54 +0200
Subject: [PATCH] removed stale code in examples
---
examples/play_model.py | 18 ------------------
examples/simple_example_1.py | 6 ------
examples/simple_example_2.py | 6 ------
examples/simple_example_3.py | 4 ----
4 files changed, 34 deletions(-)
diff --git a/examples/play_model.py b/examples/play_model.py
index 1cd125b1..6997b2c0 100644
--- a/examples/play_model.py
+++ b/examples/play_model.py
@@ -28,24 +28,16 @@ class Player(object):
self.action_prob = [0] * 4
# Removing refs to a real agent for now.
- # self.agent = Agent(self.state_size, self.action_size, "FC", 0)
- # self.agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint9900.pth'))
- # self.agent.qnetwork_local.load_state_dict(torch.load(
- # '../flatland/flatland/baselines/Nets/avoid_checkpoint15000.pth'))
-
self.iFrame = 0
self.tStart = time.time()
# Reset environment
- # self.obs = self.env.reset()
self.env.obs_builder.reset()
self.obs = self.env._get_observations()
for envAgent in range(self.env.get_num_agents()):
norm = max(1, max_lt(self.obs[envAgent], np.inf))
self.obs[envAgent] = np.clip(np.array(self.obs[envAgent]) / norm, -1, 1)
- # env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
-
self.score = 0
self.env_done = 0
@@ -58,13 +50,9 @@ class Player(object):
# Pass the (stored) observation to the agent network and retrieve the action
for handle in env.get_agent_handles():
- # Real Agent
- # action = self.agent.act(np.array(self.obs[handle]), eps=self.eps)
# Random actions
- # action = random.randint(0, 3)
action = np.random.choice([0, 1, 2, 3], 1, p=[0.2, 0.1, 0.6, 0.1])[0]
# Numpy version uses single random sequence
- # action = np.random.randint(0, 4, size=1)
self.action_prob[action] += 1
self.action_dict.update({handle: action})
@@ -125,17 +113,11 @@ def main(render=True, delay=0.0, n_trials=3, n_steps=50, sGL="PILSVG"):
oPlayer.reset()
env_renderer.set_new_rail()
- # 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(n_steps):
oPlayer.step()
if render:
env_renderer.renderEnv(show=True, frames=True, iEpisode=trials, iStep=step)
- # time.sleep(10)
if delay > 0:
time.sleep(delay)
diff --git a/examples/simple_example_1.py b/examples/simple_example_1.py
index ca442873..3055e319 100644
--- a/examples/simple_example_1.py
+++ b/examples/simple_example_1.py
@@ -10,12 +10,6 @@ specs = [[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
[(7, 270), (1, 90), (1, 90), (1, 90), (2, 90), (7, 90)],
[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]]
-# CURVED RAIL + DEAD-ENDS TEST
-# specs = [[(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)],
-# [(7, 270), (1, 90), (1, 90), (8, 90), (0, 0), (0, 0)],
-# [(0, 0), (7, 270),(1, 90), (8, 180), (0, 00), (0, 0)],
-# [(0, 0), (0, 0), (0, 0), (0, 0), (0, 0), (0, 0)]]
-
env = RailEnv(width=6,
height=4,
rail_generator=rail_from_manual_specifications_generator(specs),
diff --git a/examples/simple_example_2.py b/examples/simple_example_2.py
index f1a6a7c7..8c40a349 100644
--- a/examples/simple_example_2.py
+++ b/examples/simple_example_2.py
@@ -30,12 +30,6 @@ env = RailEnv(width=10,
number_of_agents=3,
obs_builder_object=TreeObsForRailEnv(max_depth=2))
-# env = RailEnv(width=10,
-# height=10,
-# rail_generator=rail_from_list_of_saved_GridTransitionMap_generator(['examples/sample_10_10_rail.npy']),
-# number_of_agents=3,
-# obs_builder_object=TreeObsForRailEnv(max_depth=2))
-
env.reset()
env_renderer = RenderTool(env, gl="PILSVG")
diff --git a/examples/simple_example_3.py b/examples/simple_example_3.py
index 089e72b9..e20b81ea 100644
--- a/examples/simple_example_3.py
+++ b/examples/simple_example_3.py
@@ -16,10 +16,6 @@ env = RailEnv(width=7,
number_of_agents=2,
obs_builder_object=TreeObsForRailEnv(max_depth=2))
-# Print the distance map of each cell to the target of the first agent
-# for i in range(4):
-# print(env.obs_builder.distance_map[0, :, :, i])
-
# Print the observation vector for agent 0
obs, all_rewards, done, _ = env.step({0: 0})
for i in range(env.get_num_agents()):
--
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