diff --git a/torch_training/Nets/avoid_checkpoint15000.pth b/torch_training/Nets/avoid_checkpoint15000.pth
index bb681d11151a13c54c78c22ac7dd421eea45ed32..e1daf228b7f1f6b108329715c3cdbd67805e28ae 100644
Binary files a/torch_training/Nets/avoid_checkpoint15000.pth and b/torch_training/Nets/avoid_checkpoint15000.pth differ
diff --git a/torch_training/Nets/avoid_checkpoint30000.pth b/torch_training/Nets/avoid_checkpoint30000.pth
index b6a0782cc1899a1e799011d19b3a9afb5906467c..0e2c1b28c1655bc16c9339066b8d105282f14418 100644
Binary files a/torch_training/Nets/avoid_checkpoint30000.pth and b/torch_training/Nets/avoid_checkpoint30000.pth differ
diff --git a/torch_training/Nets/avoid_checkpoint60000.pth b/torch_training/Nets/avoid_checkpoint60000.pth
index 1a35def33802ce6ac4b4f5d35c0c11c7095b2927..b6f15348130b09ae8bee0adad454031fc013fabf 100644
Binary files a/torch_training/Nets/avoid_checkpoint60000.pth and b/torch_training/Nets/avoid_checkpoint60000.pth differ
diff --git a/torch_training/dueling_double_dqn.py b/torch_training/dueling_double_dqn.py
index 6c54e4ef0aed7d833c25f3eb516d2abcb3589eee..dd67b4f0d73ffe1b3f4ad3e947debf18508e78b0 100644
--- a/torch_training/dueling_double_dqn.py
+++ b/torch_training/dueling_double_dqn.py
@@ -14,7 +14,7 @@ BUFFER_SIZE = int(1e5) # replay buffer size
BATCH_SIZE = 512 # minibatch size
GAMMA = 0.99 # discount factor 0.99
TAU = 1e-3 # for soft update of target parameters
-LR = 0.5e-4 # learning rate 5
+LR = 0.5e-4 # learning rate 0.5e-4 works
UPDATE_EVERY = 10 # how often to update the network
double_dqn = True # If using double dqn algorithm
input_channels = 5 # Number of Input channels
diff --git a/torch_training/multi_agent_inference.py b/torch_training/multi_agent_inference.py
index c14672819b1c0fed58705725da6dfb1feb1b9872..2b541219e688a8b55e20b412fd91f9e8cc22b9cb 100644
--- a/torch_training/multi_agent_inference.py
+++ b/torch_training/multi_agent_inference.py
@@ -9,15 +9,15 @@ from flatland.envs.predictions import ShortestPathPredictorForRailEnv
from flatland.envs.rail_env import RailEnv
from flatland.utils.rendertools import RenderTool
from importlib_resources import path
-
+import time
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)
-"""
-file_name = "./railway/complex_scene.pkl"
+
+file_name = "./railway/simple_avoid.pkl"
env = RailEnv(width=10,
height=20,
rail_generator=rail_from_file(file_name),
@@ -27,21 +27,21 @@ y_dim = env.height
"""
-x_dim = 18 # np.random.randint(8, 20)
-y_dim = 14 # np.random.randint(8, 20)
-n_agents = 7 # np.random.randint(3, 8)
+x_dim = 10 # np.random.randint(8, 20)
+y_dim = 10 # np.random.randint(8, 20)
+n_agents = 5 # np.random.randint(3, 8)
n_goals = n_agents + np.random.randint(0, 3)
min_dist = int(0.75 * min(x_dim, y_dim))
env = RailEnv(width=x_dim,
height=y_dim,
- rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
+ rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=2, min_dist=min_dist,
max_dist=99999,
seed=0),
obs_builder_object=TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv()),
number_of_agents=n_agents)
env.reset(True, True)
-
+"""
tree_depth = 3
observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestPathPredictorForRailEnv())
env_renderer = RenderTool(env, gl="PILSVG", )
@@ -70,7 +70,7 @@ 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, "FC", 0)
-with path(torch_training.Nets, "avoid_checkpoint46200.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint49000.pth") as file_in:
agent.qnetwork_local.load_state_dict(torch.load(file_in))
record_images = False
@@ -93,14 +93,14 @@ for trials in range(1, n_trials + 1):
if record_images:
env_renderer.gl.save_image("./Images/Avoiding/flatland_frame_{:04d}.bmp".format(frame_step))
frame_step += 1
-
+ # time.sleep(5)
# Action
for a in range(env.get_num_agents()):
action = agent.act(agent_obs[a], eps=0)
action_dict.update({a: action})
# Environment step
-
next_obs, all_rewards, done, _ = env.step(action_dict)
+
for a in range(env.get_num_agents()):
agent_obs[a] = normalize_observation(next_obs[a], observation_radius=10)
diff --git a/torch_training/multi_agent_training.py b/torch_training/multi_agent_training.py
index 7659b2da5b7ddaedbda39357971b6cb58efb2eb9..5e194f51b09bbe4e81e257caedf3d57d17e04bca 100644
--- a/torch_training/multi_agent_training.py
+++ b/torch_training/multi_agent_training.py
@@ -35,8 +35,8 @@ def main(argv):
np.random.seed(1)
# Initialize a random map with a random number of agents
- x_dim = np.random.randint(8, 20)
- y_dim = np.random.randint(8, 20)
+ x_dim = np.random.randint(8, 15)
+ y_dim = np.random.randint(8, 15)
n_agents = np.random.randint(3, 8)
n_goals = n_agents + np.random.randint(0, 3)
min_dist = int(0.75 * min(x_dim, y_dim))
@@ -53,7 +53,7 @@ def main(argv):
env = RailEnv(width=x_dim,
height=y_dim,
- rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
+ rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=2, min_dist=min_dist,
max_dist=99999,
seed=0),
obs_builder_object=observation_helper,
@@ -92,7 +92,7 @@ def main(argv):
agent = Agent(state_size, action_size, "FC", 0)
# Here you can pre-load an agent
- if True:
+ if False:
with path(torch_training.Nets, "avoid_checkpoint2400.pth") as file_in:
agent.qnetwork_local.load_state_dict(torch.load(file_in))
@@ -103,14 +103,14 @@ def main(argv):
and the size of the levels every 50 episodes.
"""
if episodes % 50 == 0:
- x_dim = np.random.randint(8, 20)
- y_dim = np.random.randint(8, 20)
+ x_dim = np.random.randint(8, 15)
+ y_dim = np.random.randint(8, 15)
n_agents = np.random.randint(3, 8)
n_goals = n_agents + np.random.randint(0, 3)
min_dist = int(0.75 * min(x_dim, y_dim))
env = RailEnv(width=x_dim,
height=y_dim,
- rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=5, min_dist=min_dist,
+ rail_generator=complex_rail_generator(nr_start_goal=n_goals, nr_extra=2, min_dist=min_dist,
max_dist=99999,
seed=0),
obs_builder_object=observation_helper,
diff --git a/torch_training/railway/very_complex.pkl b/torch_training/railway/very_complex.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0abbbe9674f08de518f99df46cc17b70b23b464b
Binary files /dev/null and b/torch_training/railway/very_complex.pkl differ