From bd5177e8ea72fcdd6d5104b080a9db65b04700eb Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Tue, 23 Apr 2019 11:25:20 +0200
Subject: [PATCH] added action choice probability for debugging purposes.
---
examples/training_navigation.py | 28 +++++++++++++++++-----------
1 file changed, 17 insertions(+), 11 deletions(-)
diff --git a/examples/training_navigation.py b/examples/training_navigation.py
index 2071a5d..eddb907 100644
--- a/examples/training_navigation.py
+++ b/examples/training_navigation.py
@@ -10,8 +10,8 @@ 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
- 1.0, # Case 1 - straight
+transition_probability = [10.0, # empty cell - Case 0
+ 50.0, # Case 1 - straight
1.0, # Case 2 - simple switch
0.3, # Case 3 - diamond drossing
0.5, # Case 4 - single slip
@@ -38,13 +38,17 @@ scores_window = deque(maxlen=100)
done_window = deque(maxlen=100)
scores = []
dones_list = []
-
+action_prob = [0]*4
agent = Agent(state_size, action_size, "FC", 0)
for trials in range(1, n_trials + 1):
# Reset environment
obs = env.reset()
+ for a in range(env.number_of_agents):
+ if np.max(obs[a]) > 0 and np.max(obs[a]) < np.inf:
+ obs[a] = np.clip(obs[a] / np.max(obs[a]), -1, 1)
+
# env.obs_builder.util_print_obs_subtree(tree=obs[0], num_elements_per_node=5)
score = 0
@@ -52,24 +56,26 @@ for trials in range(1, n_trials + 1):
# Run episode
for step in range(100):
- if trials > 114:
- env_renderer.renderEnv(show=True)
+ #if trials > 114:
+ # env_renderer.renderEnv(show=True)
# Action
for a in range(env.number_of_agents):
action = agent.act(np.array(obs[a]), eps=eps)
+ action_prob[action] += 1
action_dict.update({a: action})
# Environment step
next_obs, all_rewards, done, _ = env.step(action_dict)
-
+ for a in range(env.number_of_agents):
+ if np.max(next_obs[a]) > 0 and np.max(next_obs[a]) < np.inf:
+ next_obs[a] = np.clip(next_obs[a] / np.max(next_obs[a]), -1, 1)
# Update replay buffer and train agent
for a in range(env.number_of_agents):
agent.step(obs[a], action_dict[a], all_rewards[a], next_obs[a], done[a])
score += all_rewards[a]
obs = next_obs.copy()
-
if done['__all__']:
env_done = 1
break
@@ -81,24 +87,24 @@ 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(
+ print('\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
env.number_of_agents,
trials,
np.mean(
scores_window),
100 * np.mean(
done_window),
- eps),
+ eps, action_prob/np.sum(action_prob)),
end=" ")
if trials % 100 == 0:
print(
- '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f}'.format(
+ '\rTraining {} Agents.\tEpisode {}\tAverage Score: {:.0f}\tDones: {:.2f}%\tEpsilon: {:.2f} \t Action Probabilities: \t {}'.format(
env.number_of_agents,
trials,
np.mean(
scores_window),
100 * np.mean(
done_window),
- eps))
+ eps, action_prob / np.sum(action_prob)))
torch.save(agent.qnetwork_local.state_dict(),
'../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
--
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