From 446e6987cb0c83db6fac2e6570be0d0e80525cf4 Mon Sep 17 00:00:00 2001
From: u214892 <u214892@sbb.ch>
Date: Thu, 11 Jul 2019 09:55:50 +0200
Subject: [PATCH] #42 run baselines in ci
---
torch_training/bla.py | 24 ++++++++++++------------
1 file changed, 12 insertions(+), 12 deletions(-)
diff --git a/torch_training/bla.py b/torch_training/bla.py
index f0e7f59..f4f7131 100644
--- a/torch_training/bla.py
+++ b/torch_training/bla.py
@@ -157,18 +157,18 @@ def main(argv):
action_prob[action] += 1
action_dict.update({a: action})
# Environment step
- #
- # next_obs, all_rewards, done, _ = env.step(action_dict)
- # # print(all_rewards,action)
- # obs_original = next_obs.copy()
- # for a in range(env.get_num_agents()):
- # data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
- # current_depth=0)
- # data = norm_obs_clip(data)
- # distance = norm_obs_clip(distance)
- # agent_data = np.clip(agent_data, -1, 1)
- # next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
- # time_obs.append(next_obs)
+
+ next_obs, all_rewards, done, _ = env.step(action_dict)
+ # print(all_rewards,action)
+ obs_original = next_obs.copy()
+ for a in range(env.get_num_agents()):
+ data, distance, agent_data = split_tree(tree=np.array(next_obs[a]),
+ current_depth=0)
+ data = norm_obs_clip(data)
+ distance = norm_obs_clip(distance)
+ agent_data = np.clip(agent_data, -1, 1)
+ next_obs[a] = np.concatenate((np.concatenate((data, distance)), agent_data))
+ time_obs.append(next_obs)
#
# # Update replay buffer and train agent
# for a in range(env.get_num_agents()):
--
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