Deep Q-Learning Network From Scratch in Python, TensorFlow, and OpenAI Gym – Part 2
Welcome to Part 2 of our tutorial on building a Deep Q-Learning Network from scratch in Python, TensorFlow, and OpenAI Gym. In this part, we will dive deeper into the implementation of the DQN algorithm and train our agent to play a game in the OpenAI Gym environment.
Setting up the Environment
Before we start training our agent, we need to set up the OpenAI Gym environment and define our neural network architecture. We will use the CartPole game as our example environment.
Code Snippet:
import gym
import numpy as np
import tensorflow as tf
env = gym.make('CartPole-v1')
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
# Define the neural network architecture
model = tf.keras.Sequential([
tf.keras.layers.Dense(128, activation='relu', input_shape=(state_dim,)),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(action_dim)
])
Training the Agent
Now that we have set up the environment and defined our neural network architecture, we can start training our agent using the DQN algorithm. The training process involves interacting with the environment, gathering experiences, and updating the Q-values of the neural network.
Code Snippet:
# Define the DQN algorithm parameters
epsilon = 1.0
epsilon_min = 0.01
epsilon_decay = 0.995
gamma = 0.99
batch_size = 32
memory = deque(maxlen=10000)
# Implement the training loop
for episode in range(num_episodes):
state = env.reset()
total_reward = 0
done = False
while not done:
if np.random.rand() batch_size:
minibatch = random.sample(memory, batch_size)
train_model(model, minibatch, gamma)
state = next_state
total_reward += reward
epsilon = max(epsilon_min, epsilon * epsilon_decay)
Conclusion
Congratulations! You have successfully implemented a Deep Q-Learning Network from scratch in Python, TensorFlow, and OpenAI Gym. By training your agent to play the CartPole game, you have gained hands-on experience with reinforcement learning algorithms and neural network training. Keep experimenting and exploring new environments to further enhance your skills in this exciting field.
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Thank you so much for such a clear explanation! It really helped clear up a lot of my questions. Around the 12:40 mark, when you were discussing the use of the cost function for optimization, I got a bit curious. Why aren't all the outputs from the online and target networks being used? Could you help me understand how to decide which ones should be used? Thanks a bunch!
I have a basic question. When I have Training my Target Network once (one mini Batch for example) and done the optimizer and update weights for the Target Network one time only. Should i then exit the Training loop and go back to play and fill up replay buffert with New data. Or should I stay in Training loop serveral mini batches until my ramdom sampler ensure have go through the whole memory replay buffert befor start New real play. Best regards
❤🎉😊 I like video 11:00 and forward in your explaination about Matrix you draw and connected to cost function. It clear up lot of my question reagading DQN algoritm. Thanks
kindly upload more RL algorithms!!
Thank you very much for the interesting video!
Is the "gather_nd" neccesarry for calculating the loss function because then I get the ValueError: indices.shape[-1] must be <= params.rank, but saw indices shape: [100,9] and params shape: [100,9] for '{{node my_loss_fn/GatherNd}} = GatherNd[Tindices=DT_INT32, Tparams=DT_FLOAT](IteratorGetNext:1, my_loss_fn/GatherNd/indices)' with input shapes: [100,9], [100,9].
But if I don't use the gater_nd it gifs me no error
Why do you use a custom loss function instead of the built-in MSE function?
How do you know the amount of layers and neurons to set for your problem? My state space is 2 and action space 3, is there a way to calculate the amount of layers&neurons? 😊
Very underrated youtube channel! One of the best teachers on this platform!
The first part is given here: https://www.youtube.com/watch?v=xER1otedZOQ