Update to scriptability.

environment_wrapper.py

 """ Wrapper to abstract different learning environments for an agent. """
+import os
 import numpy as np
-
 from tqdm import trange
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -9,6 +9,7 @@ class Config:
     render = False
     force_cpu = True
     env = None
+    agent = None
     env_type = 'Lunar'
     name = 'ConfigTest'
     learn = True
@@ -24,7 +25,12 @@ class Config:
     load_from = 'agnt'
     save_to = 'saved_agents/'
 
+
     def conf_to_name(self):
+        # 0. Allow GPU usage or force tensorflow to use the CPU.
+        if self.force_cpu:
+            os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+            os.environ["CUDA_VISIBLE_DEVICES"] = ""
         self.name += self.env_type
         for layer in self.net_layout:
             self.name += '_' + str(layer) + '_'
@@ -85,15 +91,15 @@ def run(environment, agent, episodes, render=True, learn=True):
     score_history = []
     avg_score_history = []
     
-    pbar = trange(episodes, desc='Score [actual, average]: [0, 0]', unit="Episodes")
+    pbar = trange(episodes, desc=agent.name + ' [actual, average]: [0, 0]', unit="Episodes")
     for _ in pbar:
         score = one_episode(environment, agent, render, learn)
         score_history.append(score)
 
-        is_solved = np.mean(score_history[-50:])
+        is_solved = np.mean(score_history[-100:])
         avg_score_history.append(is_solved)
 
-        if is_solved > 200 and learn:
+        if is_solved > 195 and learn:
             break
         desc = ("Score [actual, average]: [{0:.2f}, {1:.2f}]".format(score, is_solved))
         pbar.set_description(desc)

main.py

@@ -6,19 +6,14 @@ import os
 import atexit
 import gym
 
-from agents import QAgent as QAgent
 import environment_wrapper as ew
 
 def run(conf):
-    # 0. Allow GPU usage or force tensorflow to use the CPU.
-    if conf.force_cpu:
-        os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
-        os.environ["CUDA_VISIBLE_DEVICES"] = ""
 
-    # 2. Create a learning agent
-    marvin = QAgent(conf) #conf.env.action_space.n, conf.env.observation_space.shape[0], conf.name)
+    # 1. Create a learning agent
+    marvin = conf.agent
 
-    # (2.5) *optional* Load agent memory and/or net from disk.
+    # (2.) *optional* Load agent memory and/or net from disk.
     if conf.load_ann or conf.load_mem:
         marvin.load(conf.save_to + conf.load_from + '/' + conf.load_from, net=conf.load_ann, memory=conf.load_mem)