chore: import upstream snapshot with attribution

examples/rl_deep_q_learning.py

@@ -0,0 +1,37 @@
+import logging
+
+from mla.neuralnet import NeuralNet
+from mla.neuralnet.layers import Activation, Dense
+from mla.neuralnet.optimizers import Adam
+from mla.rl.dqn import DQN
+
+logging.basicConfig(level=logging.CRITICAL)
+
+
+def mlp_model(n_actions, batch_size=64):
+    model = NeuralNet(
+        layers=[Dense(32), Activation("relu"), Dense(n_actions)],
+        loss="mse",
+        optimizer=Adam(),
+        metric="mse",
+        batch_size=batch_size,
+        max_epochs=1,
+        verbose=False,
+    )
+    return model
+
+
+model = DQN(n_episodes=2500, batch_size=64)
+model.init_environment("CartPole-v0")
+model.init_model(mlp_model)
+
+try:
+    # Train the model
+    # It can take from 300 to 2500 episodes to solve CartPole-v0 problem due to randomness of environment.
+    # You can stop training process using Ctrl+C signal
+    # Read more about this problem: https://gym.openai.com/envs/CartPole-v0
+    model.train(render=False)
+except KeyboardInterrupt:
+    pass
+# Render trained model
+model.play(episodes=100)