From 22c5f02c89ef5928d15b1788cec65da105c38c1a Mon Sep 17 00:00:00 2001
From: u214892 <u214892@sbb.ch>
Date: Thu, 11 Jul 2019 09:17:23 +0200
Subject: [PATCH] #42 run baselines in ci
---
torch_training/bla.py | 80 +++++++++++++++++++++----------------------
1 file changed, 40 insertions(+), 40 deletions(-)
diff --git a/torch_training/bla.py b/torch_training/bla.py
index 0f5d759..4225d66 100644
--- a/torch_training/bla.py
+++ b/torch_training/bla.py
@@ -148,46 +148,46 @@ def main(argv):
env_renderer.gl.saveImage("./Images/flatland_frame_{:04d}.bmp".format(frame_step))
frame_step += 1
# print(step)
- # Action
- for a in range(env.get_num_agents()):
- if demo:
- eps = 0
- # action = agent.act(np.array(obs[a]), eps=eps)
- action = agent.act(agent_obs[a], eps=eps)
- 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)
-
- # Update replay buffer and train agent
- for a in range(env.get_num_agents()):
- agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
- if done[a]:
- final_obs[a] = agent_obs[a].copy()
- final_obs_next[a] = agent_next_obs[a].copy()
- final_action_dict.update({a: action_dict[a]})
- if not demo and not done[a]:
- agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a])
- score += all_rewards[a] / env.get_num_agents()
-
- agent_obs = agent_next_obs.copy()
- if done['__all__']:
- env_done = 1
- for a in range(env.get_num_agents()):
- agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a])
- break
- # Epsilon decay
+ # # Action
+ # for a in range(env.get_num_agents()):
+ # if demo:
+ # eps = 0
+ # # action = agent.act(np.array(obs[a]), eps=eps)
+ # action = agent.act(agent_obs[a], eps=eps)
+ # 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)
+ #
+ # # Update replay buffer and train agent
+ # for a in range(env.get_num_agents()):
+ # agent_next_obs[a] = np.concatenate((time_obs[0][a], time_obs[1][a]))
+ # if done[a]:
+ # final_obs[a] = agent_obs[a].copy()
+ # final_obs_next[a] = agent_next_obs[a].copy()
+ # final_action_dict.update({a: action_dict[a]})
+ # if not demo and not done[a]:
+ # agent.step(agent_obs[a], action_dict[a], all_rewards[a], agent_next_obs[a], done[a])
+ # score += all_rewards[a] / env.get_num_agents()
+ #
+ # agent_obs = agent_next_obs.copy()
+ # if done['__all__']:
+ # env_done = 1
+ # for a in range(env.get_num_agents()):
+ # agent.step(final_obs[a], final_action_dict[a], all_rewards[a], final_obs_next[a], done[a])
+ # break
+ # # Epsilon decay
# eps = max(eps_end, eps_decay * eps) # decrease epsilon
#
# done_window.append(env_done)
--
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