#42 run baselines in ci

torch_training/multi_agent_training.py

@@ -7,6 +7,7 @@ import random
 import torch
 from dueling_double_dqn import Agent
 
+import torch_training
 from flatland.envs.generators import complex_rail_generator
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
@@ -63,7 +64,7 @@ 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_checkpoint30000.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint30000.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))
 
 demo = True

torch_training/training_navigation.py

@@ -7,6 +7,8 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 from dueling_double_dqn import Agent
+
+import torch_training
 from flatland.envs.generators import complex_rail_generator
 from flatland.envs.observations import TreeObsForRailEnv
 from flatland.envs.predictions import ShortestPathPredictorForRailEnv
@@ -89,7 +91,7 @@ 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_checkpoint30000.pth") as file_in:
+with path(torch_training.Nets, "avoid_checkpoint30000.pth") as file_in:
     agent.qnetwork_local.load_state_dict(torch.load(file_in))