Hi I am trying to add a qnode to keras actor-critic model. It works with angle embedding but with amplitude embedding facing some difficulty. Hoping to get some help from the community. Thanks
Code below
import gym
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import pennylane as qml
import keras_metrics
Configuration parameters for the whole setup
seed = 42
gamma = 0.99 # Discount factor for past rewards
max_steps_per_episode = 10000
env = gym.make(“CartPole-v0”) # Create the environment
env.seed(seed)
eps = np.finfo(np.float32).eps.item() # Smallest number such that 1.0 + eps != 1.0
n_qubits = 2
dev = qml.device(“default.qubit”, wires=n_qubits)
@qml.qnode(dev)
def qnode(weights, inputs=None):
qml.templates.AmplitudeEmbedding(features=inputs, wires=range(n_qubits), normalize=True)
qml.templates.StronglyEntanglingLayers(weights, wires=range(n_qubits))
return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]
n_layers = 1
weight_shapes = {“weights”: (n_layers,n_qubits,3)}
qlayer = qml.qnn.KerasLayer(qnode, weight_shapes, output_dim=n_qubits)
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
num_inputs = 4
num_actions = 2
num_hidden = 4
inputs = layers.Input(shape=(num_inputs,))
input1 = tf.keras.layers.Dense(4,activation=‘relu’,input_dim=4, trainable=False)(inputs)
common = layers.Dense(num_hidden, activation=“relu”)(qlayer(input1))
action = layers.Dense(num_actions, activation=“softmax”)(common)
critic = layers.Dense(1)(common)
model = keras.Model(inputs=inputs, outputs=[action, critic])
optimizer = keras.optimizers.Adam(learning_rate=0.01)
huber_loss = keras.losses.Huber()
action_probs_history =
critic_value_history =
rewards_history =
running_reward = 0
episode_count = 0
while True: # Run until solved
state = env.reset()
episode_reward = 0
with tf.GradientTape() as tape:
for timestep in range(1, max_steps_per_episode):
# env.render(); Adding this line would show the attempts
# of the agent in a pop up window.
state = tf.convert_to_tensor(state)
state = tf.expand_dims(state, 0)
# Predict action probabilities and estimated future rewards
# from environment state
action_probs, critic_value = model(state)
critic_value_history.append(critic_value[0, 0])
# Sample action from action probability distribution
action = np.random.choice(num_actions, p=np.squeeze(action_probs))
action_probs_history.append(tf.math.log(action_probs[0, action]))
# Apply the sampled action in our environment
state, reward, done, _ = env.step(action)
rewards_history.append(reward)
episode_reward += reward
if done:
break
# Update running reward to check condition for solving
running_reward = 0.05 * episode_reward + (1 - 0.05) * running_reward
# Calculate expected value from rewards
# - At each timestep what was the total reward received after that timestep
# - Rewards in the past are discounted by multiplying them with gamma
# - These are the labels for our critic
returns = []
discounted_sum = 0
for r in rewards_history[::-1]:
discounted_sum = r + gamma * discounted_sum
returns.insert(0, discounted_sum)
# Normalize
returns = np.array(returns)
returns = (returns - np.mean(returns)) / (np.std(returns) + eps)
returns = returns.tolist()
# Calculating loss values to update our network
history = zip(action_probs_history, critic_value_history, returns)
actor_losses = []
critic_losses = []
for log_prob, value, ret in history:
# At this point in history, the critic estimated that we would get a
# total reward = `value` in the future. We took an action with log probability
# of `log_prob` and ended up recieving a total reward = `ret`.
# The actor must be updated so that it predicts an action that leads to
# high rewards (compared to critic's estimate) with high probability.
diff = ret - value
actor_losses.append(-log_prob * diff) # actor loss
# The critic must be updated so that it predicts a better estimate of
# the future rewards.
critic_losses.append(
huber_loss(tf.expand_dims(value, 0), tf.expand_dims(ret, 0))
)
# Backpropagation
loss_value = sum(actor_losses) + sum(critic_losses)
grads = tape.gradient(loss_value, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
# Clear the loss and reward history
action_probs_history.clear()
critic_value_history.clear()
rewards_history.clear()
# Log details
episode_count += 1
if episode_count % 10 == 0:
template = "running reward: {:.2f} at episode {}"
print(template.format(running_reward, episode_count))
if running_reward > 195: # Condition to consider the task solved
print("Solved at episode {}!".format(episode_count))
break
Error : Tensor conversion requested dtype complex128 for Tensor with dtype float32: <tf.Tensor: shape=(4,), dtype=float32, numpy=array([0. , 0.42925665, 0.8368186 , 0.33981368], dtype=float32)>
The classical version : Actor Critic Method
Thanks in Advance 