From dae5e4835c80157ae70b43515331756fb77b72ba Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Thu, 25 Apr 2019 11:58:03 +0200
Subject: [PATCH] minor updates
---
examples/training_navigation.py | 39 +++++++++++++++++++++++----------
flatland/envs/rail_env.py | 3 ++-
2 files changed, 30 insertions(+), 12 deletions(-)
diff --git a/examples/training_navigation.py b/examples/training_navigation.py
index b103251..0e0f439 100644
--- a/examples/training_navigation.py
+++ b/examples/training_navigation.py
@@ -13,15 +13,15 @@ np.random.seed(1)
transition_probability = [0.5, # empty cell - Case 0
1.0, # Case 1 - straight
1.0, # Case 2 - simple switch
- 0.3, # Case 3 - diamond drossing
+ 0.3, # Case 3 - diamond crossing
0.5, # Case 4 - single slip
0.5, # Case 5 - double slip
0.2, # Case 6 - symmetrical
0.0] # Case 7 - dead end
# Example generate a random rail
-env = RailEnv(width=7,
- height=7,
+env = RailEnv(width=20,
+ height=20,
rail_generator=random_rail_generator(cell_type_relative_proportion=transition_probability),
number_of_agents=1)
env_renderer = RenderTool(env)
@@ -29,7 +29,7 @@ handle = env.get_agent_handles()
state_size = 105
action_size = 4
-n_trials = 9999
+n_trials = 15000
eps = 1.
eps_end = 0.005
eps_decay = 0.998
@@ -40,19 +40,34 @@ scores = []
dones_list = []
action_prob = [0]*4
agent = Agent(state_size, action_size, "FC", 0)
-agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint9900.pth'))
+agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint15000.pth'))
+
+demo = True
def max_lt(seq, val):
"""
Return greatest item in seq for which item < val applies.
None is returned if seq was empty or all items in seq were >= val.
"""
+ max = 0
+ idx = len(seq)-1
+ while idx >= 0:
+ if seq[idx] < val and seq[idx] >= 0 and seq[idx] > max:
+ max = seq[idx]
+ idx -= 1
+ return max
+def min_lt(seq, val):
+ """
+ Return smallest item in seq for which item > val applies.
+ None is returned if seq was empty or all items in seq were >= val.
+ """
+ min = np.inf
idx = len(seq)-1
while idx >= 0:
- if seq[idx] < val and seq[idx] >= 0:
- return seq[idx]
+ if seq[idx] > val and seq[idx] < min:
+ min = seq[idx]
idx -= 1
- return None
+ return min
for trials in range(1, n_trials + 1):
@@ -69,12 +84,14 @@ for trials in range(1, n_trials + 1):
# Run episode
for step in range(50):
- #if trials > 114:
- env_renderer.renderEnv(show=True)
+ if demo:
+ env_renderer.renderEnv(show=True)
#print(step)
# Action
for a in range(env.number_of_agents):
- action = agent.act(np.array(obs[a]), eps=0)
+ if demo:
+ eps = 0
+ action = agent.act(np.array(obs[a]), eps=eps)
action_prob[action] += 1
action_dict.update({a: action})
diff --git a/flatland/envs/rail_env.py b/flatland/envs/rail_env.py
index cf20603..9fd8585 100644
--- a/flatland/envs/rail_env.py
+++ b/flatland/envs/rail_env.py
@@ -649,7 +649,8 @@ class RailEnv(Environment):
# if agent is not in target position, add step penalty
if self.agents_position[i][0] == self.agents_target[i][0] and \
- self.agents_position[i][1] == self.agents_target[i][1]:
+ self.agents_position[i][1] == self.agents_target[i][1] and \
+ action_dict[handle] == 0:
self.dones[handle] = True
else:
self.rewards_dict[handle] += step_penalty
--
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