From df0b0ef116980c363e4f442eed7c376efbd8af96 Mon Sep 17 00:00:00 2001
From: Erik Nygren <erik.nygren@sbb.ch>
Date: Sun, 6 Oct 2019 11:20:57 -0400
Subject: [PATCH] updated training file
---
torch_training/render_agent_behavior.py | 8 ++++----
torch_training/training_navigation.py | 25 +++++++++++--------------
2 files changed, 15 insertions(+), 18 deletions(-)
diff --git a/torch_training/render_agent_behavior.py b/torch_training/render_agent_behavior.py
index 6802a22..93f9f12 100644
--- a/torch_training/render_agent_behavior.py
+++ b/torch_training/render_agent_behavior.py
@@ -28,8 +28,8 @@ y_dim = env.height
"""
# Parameters for the Environment
-x_dim = 20
-y_dim = 20
+x_dim = 25
+y_dim = 25
n_agents = 1
n_goals = 5
min_dist = 5
@@ -48,9 +48,9 @@ stochastic_data = {'prop_malfunction': 0.0, # Percentage of defective agents
TreeObservation = TreeObsForRailEnv(max_depth=2)
# Different agent types (trains) with different speeds.
-speed_ration_map = {1.: 1., # Fast passenger train
+speed_ration_map = {1.: 0., # Fast passenger train
1. / 2.: 0.0, # Fast freight train
- 1. / 3.: 0.0, # Slow commuter train
+ 1. / 3.: 1.0, # Slow commuter train
1. / 4.: 0.0} # Slow freight train
env = RailEnv(width=x_dim,
diff --git a/torch_training/training_navigation.py b/torch_training/training_navigation.py
index 3220b94..229c804 100644
--- a/torch_training/training_navigation.py
+++ b/torch_training/training_navigation.py
@@ -35,8 +35,8 @@ def main(argv):
np.random.seed(1)
# Parameters for the Environment
- x_dim = 20
- y_dim = 20
+ x_dim = 30
+ y_dim = 30
n_agents = 1
@@ -63,7 +63,7 @@ def main(argv):
seed=1, # Random seed
grid_mode=False,
max_rails_between_cities=2,
- max_rails_in_city=2),
+ max_rails_in_city=3),
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=n_agents,
stochastic_data=stochastic_data, # Malfunction data generator
@@ -105,7 +105,8 @@ def main(argv):
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
agent_obs_buffer = [None] * env.get_num_agents()
- agent_action_buffer = [None] * env.get_num_agents()
+ agent_action_buffer = [2] * env.get_num_agents()
+ agent_done_buffer = [False] * env.get_num_agents()
cummulated_reward = np.zeros(env.get_num_agents())
# Now we load a Double dueling DQN agent
@@ -115,7 +116,7 @@ def main(argv):
# Reset environment
obs, info = env.reset(True, True)
-
+ env_renderer.reset()
# Build agent specific observations
for a in range(env.get_num_agents()):
agent_obs[a] = normalize_observation(obs[a], tree_depth, observation_radius=10)
@@ -132,36 +133,32 @@ def main(argv):
if info['action_required'][a]:
action = agent.act(agent_obs[a], eps=eps)
action_prob[action] += 1
- if step == 0:
- agent_action_buffer[a] = action
else:
action = 0
action_dict.update({a: action})
# Environment step
next_obs, all_rewards, done, info = env.step(action_dict)
-
# Build agent specific observations and normalize
for a in range(env.get_num_agents()):
# Penalize waiting in order to get agent to move
if env.agents[a].status == 0:
all_rewards[a] -= 1
-
- agent_next_obs[a] = normalize_observation(next_obs[a], tree_depth, observation_radius=10)
+ if info['action_required'][a]:
+ agent_next_obs[a] = normalize_observation(next_obs[a], tree_depth, observation_radius=10)
cummulated_reward[a] += all_rewards[a]
# Update replay buffer and train agent
for a in range(env.get_num_agents()):
- if (agent_obs_buffer[a] is not None and info['action_required'][a] and env.agents[a].status != 3) or \
- env.agents[a].status == 2:
+ if (info['action_required'][a] and env.agents[a].status != 3) or env.agents[a].status == 2:
- agent_delayed_next = agent_obs[a].copy()
agent.step(agent_obs_buffer[a], agent_action_buffer[a], all_rewards[a],
- agent_delayed_next, done[a])
+ agent_obs[a], agent_done_buffer[a])
cummulated_reward[a] = 0.
if info['action_required'][a]:
agent_obs_buffer[a] = agent_obs[a].copy()
agent_action_buffer[a] = action_dict[a]
+ agent_done_buffer[a] = done[a]
score += all_rewards[a] / env.get_num_agents()
--
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