moves batch update logic to dqn_batch.py

dqn.py

@@ -42,23 +42,16 @@ def act(self, state):
 
     def replay(self, batch_size):
         minibatch = random.sample(self.memory, batch_size)
-        states, targets_f = [], []
         for state, action, reward, next_state, done in minibatch:
             target = reward
             if not done:
                 target = (reward + self.gamma *
                           np.amax(self.model.predict(next_state)[0]))
             target_f = self.model.predict(state)
-            target_f[0][action] = target 
-            # Filtering out states and targets for training
-            states.append(state[0])
-            targets_f.append(target_f[0])
-        history = self.model.fit(np.array(states), np.array(targets_f), epochs=1, verbose=0)
-        # Keeping track of loss
-        loss = history.history['loss'][0]
+            target_f[0][action] = target
+            self.model.fit(state, target_f, epochs=1, verbose=0)
         if self.epsilon > self.epsilon_min:
             self.epsilon *= self.epsilon_decay
-        return loss
 
     def load(self, name):
         self.model.load_weights(name)
@@ -92,10 +85,6 @@ def save(self, name):
                       .format(e, EPISODES, time, agent.epsilon))
                 break
             if len(agent.memory) > batch_size:
-                loss = agent.replay(batch_size)
-                # Logging training loss every 10 timesteps
-                if time % 10 == 0:
-                    print("episode: {}/{}, time: {}, loss: {:.4f}"
-                        .format(e, EPISODES, time, loss))  
+                agent.replay(batch_size)
         # if e % 10 == 0:
-        #     agent.save("./save/cartpole-dqn.h5")
+        #     agent.save("./save/cartpole-dqn.h5")

dqn_batch.py

@@ -0,0 +1,101 @@
+# -*- coding: utf-8 -*-
+import random
+import gym
+import numpy as np
+from collections import deque
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.optimizers import Adam
+
+EPISODES = 1000
+
+class DQNAgent:
+    def __init__(self, state_size, action_size):
+        self.state_size = state_size
+        self.action_size = action_size
+        self.memory = deque(maxlen=2000)
+        self.gamma = 0.95    # discount rate
+        self.epsilon = 1.0  # exploration rate
+        self.epsilon_min = 0.01
+        self.epsilon_decay = 0.995
+        self.learning_rate = 0.001
+        self.model = self._build_model()
+
+    def _build_model(self):
+        # Neural Net for Deep-Q learning Model
+        model = Sequential()
+        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
+        model.add(Dense(24, activation='relu'))
+        model.add(Dense(self.action_size, activation='linear'))
+        model.compile(loss='mse',
+                      optimizer=Adam(lr=self.learning_rate))
+        return model
+
+    def remember(self, state, action, reward, next_state, done):
+        self.memory.append((state, action, reward, next_state, done))
+
+    def act(self, state):
+        if np.random.rand() <= self.epsilon:
+            return random.randrange(self.action_size)
+        act_values = self.model.predict(state)
+        return np.argmax(act_values[0])  # returns action
+
+    def replay(self, batch_size):
+        minibatch = random.sample(self.memory, batch_size)
+        states, targets_f = [], []
+        for state, action, reward, next_state, done in minibatch:
+            target = reward
+            if not done:
+                target = (reward + self.gamma *
+                          np.amax(self.model.predict(next_state)[0]))
+            target_f = self.model.predict(state)
+            target_f[0][action] = target 
+            # Filtering out states and targets for training
+            states.append(state[0])
+            targets_f.append(target_f[0])
+        history = self.model.fit(np.array(states), np.array(targets_f), epochs=1, verbose=0)
+        # Keeping track of loss
+        loss = history.history['loss'][0]
+        if self.epsilon > self.epsilon_min:
+            self.epsilon *= self.epsilon_decay
+        return loss
+
+    def load(self, name):
+        self.model.load_weights(name)
+
+    def save(self, name):
+        self.model.save_weights(name)
+
+
+if __name__ == "__main__":
+    env = gym.make('CartPole-v1')
+    state_size = env.observation_space.shape[0]
+    action_size = env.action_space.n
+    agent = DQNAgent(state_size, action_size)
+    # agent.load("./save/cartpole-dqn.h5")
+    done = False
+    batch_size = 32
+
+    for e in range(EPISODES):
+        state = env.reset()
+        state = np.reshape(state, [1, state_size])
+        for time in range(500):
+            # env.render()
+            action = agent.act(state)
+            next_state, reward, done, _ = env.step(action)
+            reward = reward if not done else -10
+            next_state = np.reshape(next_state, [1, state_size])
+            agent.remember(state, action, reward, next_state, done)
+            state = next_state
+            if done:
+                print("episode: {}/{}, score: {}, e: {:.2}"
+                      .format(e, EPISODES, time, agent.epsilon))
+                break
+            if len(agent.memory) > batch_size:
+                loss = agent.replay(batch_size)
+                # Logging training loss every 10 timesteps
+                if time % 10 == 0:
+                    print("episode: {}/{}, time: {}, loss: {:.4f}"
+                        .format(e, EPISODES, time, loss))  
+        # if e % 10 == 0:
+        #     agent.save("./save/cartpole-dqn.h5")