From 622cab240007294b181a0a941c47cd99dd6ac210 Mon Sep 17 00:00:00 2001
From: u214892 <u214892@sbb.ch>
Date: Wed, 10 Jul 2019 16:19:44 +0200
Subject: [PATCH] #42 run baselines in ci
---
torch_training/bla.py | 27 +++++++++++++++++++++++++++
1 file changed, 27 insertions(+)
diff --git a/torch_training/bla.py b/torch_training/bla.py
index a103f9f..cd1aa13 100644
--- a/torch_training/bla.py
+++ b/torch_training/bla.py
@@ -55,6 +55,33 @@ def main(argv):
number_of_agents=n_agents)
env.reset(True, True)
file_load = False
+ observation_helper = TreeObsForRailEnv(max_depth=3, predictor=ShortestPathPredictorForRailEnv())
+ env_renderer = RenderTool(env, gl="PILSVG", )
+ handle = env.get_agent_handles()
+ features_per_node = 9
+ state_size = features_per_node * 85 * 2
+ action_size = 5
+
+ print("main3")
+
+ # We set the number of episodes we would like to train on
+ if 'n_trials' not in locals():
+ n_trials = 30000
+ max_steps = int(3 * (env.height + env.width))
+ eps = 1.
+ eps_end = 0.005
+ eps_decay = 0.9995
+ action_dict = dict()
+ final_action_dict = dict()
+ scores_window = deque(maxlen=100)
+ done_window = deque(maxlen=100)
+ time_obs = deque(maxlen=2)
+ scores = []
+ dones_list = []
+ 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)
print("multi_agent_trainging.py (2)")
--
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