diff --git a/torch_training/multi_agent_inference.py b/torch_training/multi_agent_inference.py
index b376623e7ecde3bbfcf01a5eeb11e8a76c132cc7..f2458c20c2e47ea56e577f229f4221b1bfe4e195 100644
--- a/torch_training/multi_agent_inference.py
+++ b/torch_training/multi_agent_inference.py
@@ -15,63 +15,70 @@ import torch_training.Nets
from torch_training.dueling_double_dqn import Agent
from utils.observation_utils import normalize_observation
-random.seed(3)
-np.random.seed(2)
+random.seed(1)
+np.random.seed(1)
+"""
+file_name = "./railway/complex_scene.pkl"
+env = RailEnv(width=10,
+ height=20,
+ rail_generator=rail_from_file(file_name),
+ obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()))
+x_dim = env.width
+y_dim = env.height
+"""
+
# Parameters for the Environment
-x_dim = 20
-y_dim = 20
-n_agents = 5
-tree_depth = 2
+x_dim = 25
+y_dim = 25
+n_agents = 1
+
+# We are training an Agent using the Tree Observation with depth 2
+observation_builder = TreeObsForRailEnv(max_depth=2)
# Use a the malfunction generator to break agents from time to time
-stochastic_data = {'prop_malfunction': 0.1, # Percentage of defective agents
+stochastic_data = {'prop_malfunction': 0.0, # Percentage of defective agents
'malfunction_rate': 30, # Rate of malfunction occurence
'min_duration': 3, # Minimal duration of malfunction
'max_duration': 20 # Max duration of malfunction
}
# Custom observation builder
-predictor = ShortestPathPredictorForRailEnv()
-observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=predictor)
+TreeObservation = TreeObsForRailEnv(max_depth=2)
# Different agent types (trains) with different speeds.
-speed_ration_map = {1.: 0.25, # Fast passenger train
- 1. / 2.: 0.25, # Fast freight train
- 1. / 3.: 0.25, # Slow commuter train
- 1. / 4.: 0.25} # Slow freight train
+speed_ration_map = {1.: 1., # Fast passenger train
+ 1. / 2.: 0.0, # Fast freight train
+ 1. / 3.: 0.0, # Slow commuter train
+ 1. / 4.: 0.0} # Slow freight train
env = RailEnv(width=x_dim,
height=y_dim,
- rail_generator=sparse_rail_generator(num_cities=5,
+ rail_generator=sparse_rail_generator(max_num_cities=3,
# Number of cities in map (where train stations are)
- num_intersections=4,
- # Number of intersections (no start / target)
- num_trainstations=10, # Number of possible start/targets on map
- min_node_dist=3, # Minimal distance of nodes
- node_radius=2, # Proximity of stations to city center
- num_neighb=3,
- # Number of connections to other cities/intersections
- seed=15, # Random seed
- grid_mode=True,
- enhance_intersection=False
- ),
+ seed=1, # Random seed
+ grid_mode=False,
+ max_rails_between_cities=2,
+ max_rails_in_city=2),
schedule_generator=sparse_schedule_generator(speed_ration_map),
number_of_agents=n_agents,
stochastic_data=stochastic_data, # Malfunction data generator
- obs_builder_object=observation_helper)
+ obs_builder_object=TreeObservation)
env.reset(True, True)
+observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
env_renderer = RenderTool(env, gl="PILSVG", )
-handle = env.get_agent_handles()
num_features_per_node = env.obs_builder.observation_dim
+
+tree_depth = 2
nr_nodes = 0
for i in range(tree_depth + 1):
nr_nodes += np.power(4, i)
state_size = num_features_per_node * nr_nodes
action_size = 5
-n_trials = 10
-observation_radius = 10
+# We set the number of episodes we would like to train on
+if 'n_trials' not in locals():
+ n_trials = 60000
max_steps = int(3 * (env.height + env.width))
eps = 1.
eps_end = 0.005
@@ -80,14 +87,13 @@ action_dict = dict()
final_action_dict = dict()
scores_window = deque(maxlen=100)
done_window = deque(maxlen=100)
-time_obs = deque(maxlen=2)
scores = []
dones_list = []
action_prob = [0] * action_size
agent_obs = [None] * env.get_num_agents()
agent_next_obs = [None] * env.get_num_agents()
agent = Agent(state_size, action_size)
-with path(torch_training.Nets, "avoid_checkpoint500.pth") as file_in:
+with path(torch_training.Nets, "avoider_checkpoint1000.pth") as file_in:
agent.qnetwork_local.load_state_dict(torch.load(file_in))
record_images = False
@@ -97,29 +103,35 @@ for trials in range(1, n_trials + 1):
# 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], observation_radius=10)
+ agent_obs[a] = agent_obs[a] = normalize_observation(obs[a], tree_depth, observation_radius=10)
+ # Reset score and done
+ score = 0
+ env_done = 0
# Run episode
for step in range(max_steps):
- env_renderer.render_env(show=True, show_observations=False, show_predictions=True)
- if record_images:
- env_renderer.gl.save_image("./Images/Avoiding/flatland_frame_{:04d}.bmp".format(frame_step))
- frame_step += 1
- # time.sleep(1.5)
# Action
for a in range(env.get_num_agents()):
- action = agent.act(agent_obs[a], eps=0)
+ if info['action_required'][a]:
+ action = agent.act(agent_obs[a], eps=0.)
+
+ else:
+ action = 0
+
+ action_prob[action] += 1
action_dict.update({a: action})
# Environment step
- next_obs, all_rewards, done, _ = env.step(action_dict)
-
+ obs, all_rewards, done, _ = env.step(action_dict)
+ env_renderer.render_env(show=True, show_predictions=True, show_observations=False)
+ # Build agent specific observations and normalize
for a in range(env.get_num_agents()):
- agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
+ agent_obs[a] = normalize_observation(obs[a], tree_depth, observation_radius=10)
+
if done['__all__']:
break
+