#42 run baselines in ci

torch_training/multi_agent_training.py

-import random
 from collections import deque
+from sys import path
 
 import matplotlib.pyplot as plt
 import numpy as np
+import random
 import torch
 from dueling_double_dqn import Agent
+
 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
 from flatland.utils.rendertools import RenderTool
-
 from utils.observation_utils import norm_obs_clip, split_tree
 
 random.seed(1)
@@ -62,7 +63,8 @@ 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)
-agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint30000.pth'))
+with path("torch_training/Nets", "avoid_checkpoint30000.pth") as file_in:
+    agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 demo = True
 record_images = False

torch_training/training_navigation.py

+from sys import path
+
 import random
 from collections import deque
 
@@ -87,7 +89,9 @@ 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)
-agent.qnetwork_local.load_state_dict(torch.load('./Nets/avoid_checkpoint30000.pth'))
+with path("torch_training/Nets", "avoid_checkpoint30000.pth") as file_in:
+    agent.qnetwork_local.load_state_dict(torch.load(file_in))
+
 
 demo = True
 record_images = False