# Computes epsilon for epsilon greedy policy given the training step
epsilon_fn = keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=0.01, # initial ε
decay_steps=epsilon_decay_steps,
end_learning_rate=epsilon_final) # final ε
agent = DqnAgent(tf_env.time_step_spec(),
tf_env.action_spec(),
q_network=q_net,
optimizer=optimizer,
target_update_period=target_update_period,
td_errors_loss_fn=keras.losses.Huber(reduction="none"),
gamma=discount_factor, # discount factor
train_step_counter=train_step,
epsilon_greedy=lambda: epsilon_fn(train_step))
agent.policy = tf.compat.v2.saved_model.load('../DATA/policy_{}'.format(II))
agent.initialize()
# Speed up as tensorflow function
agent.train = function(agent.train)
## ------------------------------------------------------------------------------
## ------------------------------------------------------------------------------
## ------------------------------------------------------------------------------
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
# Determines the data spec type
data_spec=agent.collect_data_spec,
# The number of trajectories added at each step
batch_size=tf_env.batch_size,
# This can store 4 million trajectories (note: requires a lot of RAM)
epsilon_fn = keras.optimizers.schedules.PolynomialDecay(
initial_learning_rate=1.0, decay_steps=250000, end_learning_rate=0.01)
print("Before Agent")
agent = DqnAgent(tf_env.time_step_spec(),
tf_env.action_spec(),
q_network=q_net,
optimizer=optimizer,
target_update_period=2000,
td_errors_loss_fn=keras.losses.Huber(reduction="none"),
gamma=0.99,
train_step_counter=train_step,
epsilon_greedy=lambda: epsilon_fn(train_step))
agent.initialize()
if policy != None:
agent.policy = policy
print("After Agent.initialize()")
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=agent.collect_data_spec,
batch_size=tf_env.batch_size,
max_length=100000)
"""
replay_buffer = PyHashedReplayBuffer(
data_spec = agent.collect_data_spec,
#batch_size = tf_env.batch_size,
capacity = 1000000
)
"""
print("After replay_buffer")
