noise = np.clip(noise, -0.5, 0.5)
action += noise
action = np.clip(action, -1, 1)
action = np.reshape(action, newshape=(2, ))
# Perform action, and get new information:
new_state, reward, done, info = env.step(action)
# Save reward:
episodic_reward += reward
# Store new values in buffer:
action = np.squeeze(action)
buffer.record((state, action, reward, new_state))
# Update state with the new one:
state = new_state
""" Update / Learn """
# Sample from the buffer:
s_batch, a_batch, r_batch, ns_batch = buffer.batch_sample()
s_batch = tf.convert_to_tensor(s_batch)
a_batch = tf.convert_to_tensor(a_batch)
r_batch = tf.convert_to_tensor(r_batch)
ns_batch = tf.convert_to_tensor(ns_batch)
# Select action according to the actor/policy:
next_action = actor_target(ns_batch)
next_action = np.clip(next_action, -1, 1)
with tf.GradientTape(persistent=True) as tape:
# Target Q values via target critic networks (next state, next action)
q1_ = critic_target([ns_batch, next_action])
# Choose minimum from these two values for the double Q update rule
# Calculate actual Q value:
