enhanced functionality

sequential_agent/run_test.py

@@ -8,11 +8,11 @@ import numpy as np
 
 np.random.seed(2)
 """
-file_name = "./railway/complex_scene.pkl"
+file_name = "../torch_training/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()))
+              obs_builder_object=TreeObsForRailEnv(max_depth=1, predictor=ShortestPathPredictorForRailEnv()))
 x_dim = env.width
 y_dim = env.height
 
@@ -38,8 +38,8 @@ observation_helper = TreeObsForRailEnv(max_depth=tree_depth, predictor=ShortestP
 env_renderer = RenderTool(env, gl="PILSVG", )
 handle = env.get_agent_handles()
 n_trials = 1
-max_steps = 3 * (env.height + env.width)
-record_images = True
+max_steps = 100 * (env.height + env.width)
+record_images = False
 agent = OrderedAgent()
 action_dict = dict()
 
@@ -63,6 +63,7 @@ for trials in range(1, n_trials + 1):
         for a in range(env.get_num_agents()):
             if done[a]:
                 acting_agent += 1
+                print(acting_agent)
             if a == acting_agent:
                 action = agent.act(obs[a], eps=0)
             else:

sequential_agent/simple_order_agent.py

@@ -18,7 +18,7 @@ class OrderedAgent:
         min_dist = min_lt(distance, 0)
         min_direction = np.where(distance == min_dist)
         if len(min_direction[0]) > 1:
-            return min_direction[0][0] + 1
+            return min_direction[0][-1] + 1
         return min_direction[0] + 1
 
     def step(self, memories):

torch_training/multi_agent_inference.py

@@ -98,7 +98,6 @@ for trials in range(1, n_trials + 1):
         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)