new training files and updates to observation

torch_training/multi_agent_inference.py

@@ -3,7 +3,7 @@ from collections import deque
 
 import numpy as np
 import torch
-from flatland.envs.generators import complex_rail_generator, rail_from_file
+from flatland.envs.generators import complex_rail_generator
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
@@ -16,8 +16,8 @@ from utils.observation_utils import norm_obs_clip, split_tree
 
 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),
@@ -29,8 +29,8 @@ y_dim = env.height
 
 x_dim = 20 #np.random.randint(8, 20)
 y_dim = 20 #np.random.randint(8, 20)
-n_agents = 10 #np.random.randint(3, 8)
-n_goals = n_agents + np.random.randint(0, 3)
+n_agents = 1  # np.random.randint(3, 8)
+n_goals = 10 + n_agents + np.random.randint(0, 3)
 min_dist = int(0.75 * min(x_dim, y_dim))
 
 env = RailEnv(width=x_dim,
@@ -41,7 +41,7 @@ env = RailEnv(width=x_dim,
               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", )
@@ -53,8 +53,8 @@ for i in range(tree_depth + 1):
 state_size = num_features_per_node * nr_nodes
 action_size = 5
 
-n_trials = 1
-observation_radius = 10
+n_trials = 10
+observation_radius = 20
 max_steps = int(3 * (env.height + env.width))
 eps = 1.
 eps_end = 0.005
@@ -73,7 +73,7 @@ agent = Agent(state_size, action_size, "FC", 0)
 with path(torch_training.Nets, "avoid_checkpoint60000.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
-record_images = True
+record_images = False
 frame_step = 0
 
 for trials in range(1, n_trials + 1):
@@ -93,7 +93,7 @@ for trials in range(1, n_trials + 1):
 
     # Run episode
     for step in range(max_steps):
-        env_renderer.render_env(show=True, show_observations=False, show_predictions=False)
+        env_renderer.render_env(show=True, show_observations=True, show_predictions=False)
 
         if record_images:
             env_renderer.gl.save_image("./Images/Avoiding/flatland_frame_{:04d}.bmp".format(frame_step))
@@ -114,8 +114,7 @@ for trials in range(1, n_trials + 1):
             data = norm_obs_clip(data, fixed_radius=observation_radius)
             distance = norm_obs_clip(distance)
             agent_data = np.clip(agent_data, -1, 1)
-            agent_next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
+            agent_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
 
-        agent_obs = agent_next_obs.copy()
         if done['__all__']:
             break