minor updates

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
+from flatland.envs.generators import complex_rail_generator, rail_from_file
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
 from flatland.envs.rail_env import RailEnv
@@ -14,16 +14,17 @@ import torch_training.Nets
 from torch_training.dueling_double_dqn import Agent
 from utils.observation_utils import norm_obs_clip, split_tree
 
-random.seed(1)
-np.random.seed(1)
-"""
-file_name = "./railway/complex_scene.pkl"
+random.seed(3)
+np.random.seed(2)
+
+file_name = "./railway/navigate_and_avoid.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
+
 """
 
 x_dim = np.random.randint(8, 20)
@@ -40,7 +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", )
@@ -52,7 +53,7 @@ for i in range(tree_depth + 1):
 state_size = num_features_per_node * nr_nodes
 action_size = 5
 
-n_trials = 100
+n_trials = 5
 observation_radius = 10
 max_steps = int(3 * (env.height + env.width))
 eps = 1.
@@ -69,7 +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_checkpoint49700.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint53400.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 record_images = False
@@ -95,7 +96,7 @@ for trials in range(1, n_trials + 1):
         env_renderer.render_env(show=True, show_observations=False, show_predictions=True)
 
         if record_images:
-            env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
+            env_renderer.gl.save_image("./Images/Avoiding/flatland_frame_{:04d}.bmp".format(frame_step))
             frame_step += 1
 
         # Action