Create deep_reinforcement_learning.py

ai/models/deep_reinforcement_learning.py

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+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Conv2D, Flatten
+
+class DeepQNetwork:
+    def __init__(self, state_dim, action_dim, learning_rate=0.001, discount_factor=0.99):
+        self.state_dim = state_dim
+        self.action_dim = action_dim
+        self.learning_rate = learning_rate
+        self.discount_factor = discount_factor
+        self.model = self.create_model()
+
+    def create_model(self):
+        model = Sequential()
+        model.add(Conv2D(32, (8, 8), activation='relu', input_shape=(self.state_dim, self.state_dim, 1)))
+        model.add(Flatten())
+        model.add(Dense(64, activation='relu'))
+        model.add(Dense(self.action_dim, activation='linear'))
+        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(learning_rate=self.learning_rate))
+        return model
+
+    def remember(self, state, action, reward, next_state, done):
+        self.states.append(state)
+        self.actions.append(action)
+        self.rewards.append(reward)
+        self.next_states.append(next_state)
+        self.dones.append(done)
+
+    def train(self, batch_size):
+        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.sample_memory(batch_size)
+        target_q_values = reward_batch + self.discount_factor * np.max(self.model.predict(next_state_batch), axis=1)
+        target_q_values[done_batch] = reward_batch[done_batch]
+        self.model.fit(state_batch, target_q_values, verbose=0)
+
+    def act(self, state):
+        state = np.expand_dims(state, axis=0)
+        q_values = self.model.predict(state)
+        action = np.argmax(q_values)
+        return action
+
+    def sample_memory(self, batch_size):
+        indices = np.random.choice(len(self.states), batch_size)
+        state_batch = np.array(self.states)[indices]
+        action_batch = np.array(self.actions)[indices]
+        reward_batch = np.array(self.rewards)[indices]
+        next_state_batch = np.array(self.next_states)[indices]
+        done_batch = np.array(self.dones)[indices]
+        return state_batch, action_batch, reward_batch, next_state_batch, done_batch
+
+    def save(self, filepath):
+        self.model.save(filepath)
+
+    def load(self, filepath):
+        self.model = tf.keras.models.load_model(filepath)