Update on environment wrapper

Updated learning behaviour for double Q agent and fixed batch size for offline learning.

agents.py

@@ -78,7 +78,7 @@ class DQAgent(QAgent):
         return np.argmax(action_values[0])
 
     def learn(self, offline=False):
-        for _ in range(2):
+        for _ in range(3):
             if np.random.rand() < 0.5:
                 temp = self.q
                 self.q = self.q2
@@ -87,8 +87,8 @@ class DQAgent(QAgent):
         epochs = 1
         if offline:
             batch_size = 4096
-        if len(self.memory.history) < self.online_batch_size:
-            return 0.0
+        if len(self.memory.history) < batch_size:
+            return
         states, actions, rewards, following_states, dones = self.memory.get_batch(batch_size)
         q_max_hat = rewards + self.gamma * (np.amax(self.q2.predict_on_batch(following_states), axis=1)) * (1-dones)
         y = self.q.predict_on_batch(states)

environment_wrapper.py

@@ -76,11 +76,11 @@ def run(environment, agent, episodes, render=True, learn=True):
     return score_history, avg_score_history
 
 
-def process_logs(avg_score_history, loss, title="Title"):
+def process_logs(avg_score_history, loss, title="Title", render=False):
     """ Plot the log history """
     plt.plot([i+1 for i in range(0, len(loss), 2)], loss[::2])
     plt.plot([i+1 for i in range(0, len(avg_score_history), 2)], avg_score_history[::2], '--')
     plt.title(title)
-    plt.show()
     plt.savefig(title + '.png', format="png")
-
+    if render:
+        plt.show()

main.py

@@ -6,7 +6,7 @@ import os
 import atexit
 import gym
 
-from agents import QAgent
+from agents import DQAgent as QAgent
 import environment_wrapper as ew
 
 # Allow GPU usage or force tensorflow to use the CPU.
@@ -21,12 +21,14 @@ if __name__ == '__main__':
     env = gym.make('LunarLander-v2')
 
     # 2. Create a learning agent
-    marvin = QAgent(env.action_space.n, env.observation_space.shape[0], 'from_scratch')
+    marvin = QAgent(env.action_space.n, env.observation_space.shape[0], 'FromScratchDouble')
 
     # (2.5) *optional* Load agent memory and/or net from disk.
-    LOAD_MEMORIES = False
+    agnt = 'agent'
     LOAD_ANN = False
-    marvin.load('saved_agents/agent/agent', net=LOAD_ANN, memory=LOAD_MEMORIES)
+    LOAD_MEMORIES = False
+    if LOAD_ANN or LOAD_MEMORIES:
+        marvin.load('saved_agents/' + agnt + '/' + agnt, net=LOAD_ANN, memory=LOAD_MEMORIES)
 
     # 3. Set your configurations for the run.
     RENDER = False
@@ -60,5 +62,4 @@ if __name__ == '__main__':
         marvin.save(SAVE_PATH)
 
     # Show the result of the runl.
-    if RENDER:
-        ew.process_logs(avg_score, loss, title=marvin.name)
+    ew.process_logs(avg_score, loss, title=marvin.name, render=RENDER)