From 32d2635f126e3a55502a6bf383df3cfb6f409cb9 Mon Sep 17 00:00:00 2001
From: hagrid67 <jdhwatson@gmail.com>
Date: Fri, 17 May 2019 13:08:17 +0100
Subject: [PATCH] butchered Player to remove all trace of nn agent and torch
---
examples/play_model.py | 21 +++++++++++----------
1 file changed, 11 insertions(+), 10 deletions(-)
diff --git a/examples/play_model.py b/examples/play_model.py
index e502bd2..5ed6feb 100644
--- a/examples/play_model.py
+++ b/examples/play_model.py
@@ -1,9 +1,9 @@
from flatland.envs.rail_env import RailEnv
from flatland.envs.generators import complex_rail_generator
from flatland.utils.rendertools import RenderTool
-from flatland.baselines.dueling_double_dqn import Agent
+# from flatland.baselines.dueling_double_dqn import Agent
from collections import deque
-import torch
+# import torch
import random
import numpy as np
import time
@@ -26,7 +26,7 @@ class Player(object):
self.scores = []
self.dones_list = []
self.action_prob = [0]*4
- self.agent = Agent(self.state_size, self.action_size, "FC", 0)
+ # self.agent = Agent(self.state_size, self.action_size, "FC", 0)
# self.agent.qnetwork_local.load_state_dict(torch.load('../flatland/baselines/Nets/avoid_checkpoint9900.pth'))
#self.agent.qnetwork_local.load_state_dict(torch.load(
# '../flatland/flatland/baselines/Nets/avoid_checkpoint15000.pth'))
@@ -72,11 +72,12 @@ class Player(object):
next_obs[handle] = np.clip(np.array(next_obs[handle]) / norm, -1, 1)
# Update replay buffer and train agent
- for handle in self.env.get_agent_handles():
- self.agent.step(self.obs[handle], self.action_dict[handle],
- all_rewards[handle], next_obs[handle], done[handle],
- train=False)
- self.score += all_rewards[handle]
+ if False:
+ for handle in self.env.get_agent_handles():
+ self.agent.step(self.obs[handle], self.action_dict[handle],
+ all_rewards[handle], next_obs[handle], done[handle],
+ train=False)
+ self.score += all_rewards[handle]
self.iFrame += 1
@@ -263,8 +264,8 @@ def main_old(render=True, delay=0.0):
np.mean(scores_window),
100 * np.mean(done_window),
eps, rFps, action_prob / np.sum(action_prob)))
- torch.save(agent.qnetwork_local.state_dict(),
- '../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
+ # torch.save(agent.qnetwork_local.state_dict(),
+ # '../flatland/baselines/Nets/avoid_checkpoint' + str(trials) + '.pth')
action_prob = [1]*4
--
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