def test_agent_actions():
print()
pricing_agents = [
TaskPricingDqnAgent(0, create_lstm_dqn_network(9, 5)),
TaskPricingDdqnAgent(1, create_lstm_dqn_network(9, 5)),
TaskPricingDuelingDqnAgent(2, create_lstm_dueling_dqn_network(9, 5)),
TaskPricingCategoricalDqnAgent(
3, create_lstm_categorical_dqn_network(9, 5)),
TaskPricingDdpgAgent(4, create_lstm_actor_network(9),
create_lstm_critic_network(9)),
TaskPricingTD3Agent(5, create_lstm_actor_network(9),
create_lstm_critic_network(9),
create_lstm_critic_network(9))
]
weighting_agents = [
ResourceWeightingDqnAgent(0, create_lstm_dqn_network(16, 5)),
ResourceWeightingDdqnAgent(1, create_lstm_dqn_network(16, 5)),
ResourceWeightingDuelingDqnAgent(
2, create_lstm_dueling_dqn_network(16, 5)),
ResourceWeightingCategoricalDqnAgent(
3, create_lstm_categorical_dqn_network(16, 5)),
ResourceWeightingDdpgAgent(4, create_lstm_actor_network(16),
create_lstm_critic_network(16)),
ResourceWeightingTD3Agent(5, create_lstm_actor_network(16),
create_lstm_critic_network(16),
create_lstm_critic_network(16))
]
env, state = OnlineFlexibleResourceAllocationEnv.load_env(
'agent/settings/actions.env')
for agent in pricing_agents:
actions = {
server: agent.bid(state.auction_task, tasks, server,
state.time_step)
for server, tasks in state.server_tasks.items()
}
# noinspection PyUnboundLocalVariable
print(
f'Actions: {{{", ".join([f"{server.name}: {action}" for server, action in actions.items()])}}}'
)
state, rewards, done, _ = env.step(actions)
for agent in weighting_agents:
actions = {
server: agent.weight(tasks, server, state.time_step)
for server, tasks in state.server_tasks.items()
}
print(
f'Actions: {{{", ".join([f"{server.name}: {list(task_action.values())}" for server, task_action in actions.items()])}}}'
)
state, rewards, done, _ = env.step(actions)
writer, datetime = setup_tensorboard('training/results/logs/', folder)
save_folder = f'{folder}_{datetime}'
env = OnlineFlexibleResourceAllocationEnv([
'./training/settings/basic.env',
'./training/settings/large_tasks_servers.env',
'./training/settings/limited_resources.env',
'./training/settings/mixture_tasks_servers.env'
])
eval_envs = generate_eval_envs(env, 20,
f'./training/settings/eval_envs/algo/')
task_pricing_agents = [
TaskPricingCategoricalDqnAgent(agent_num,
create_lstm_categorical_dqn_network(
9, 21),
save_folder=save_folder)
for agent_num in range(3)
]
resource_weighting_agents = [
ResourceWeightingCategoricalDqnAgent(
0,
create_lstm_categorical_dqn_network(16, 11),
save_folder=save_folder)
]
with writer.as_default():
run_training(env, eval_envs, 600, task_pricing_agents,
resource_weighting_agents, 10)
for agent in task_pricing_agents:
def test_task_price_training():
print()
setup_tensorboard('/tmp/results/', 'price_training')
# List of agents
agents: List[TaskPricingRLAgent] = [
TaskPricingDqnAgent(0,
create_lstm_dqn_network(9, 10),
batch_size=4,
save_folder='tmp'),
TaskPricingDdqnAgent(1,
create_lstm_dqn_network(9, 10),
batch_size=4,
save_folder='tmp'),
TaskPricingDuelingDqnAgent(2,
create_lstm_dueling_dqn_network(9, 10),
batch_size=4,
save_folder='tmp'),
TaskPricingCategoricalDqnAgent(3,
create_lstm_categorical_dqn_network(
9, 10),
batch_size=4,
save_folder='tmp'),
TaskPricingDdpgAgent(4,
create_lstm_actor_network(9),
create_lstm_critic_network(9),
batch_size=4,
save_folder='tmp'),
TaskPricingTD3Agent(5,
create_lstm_actor_network(9),
create_lstm_critic_network(9),
create_lstm_critic_network(9),
batch_size=4,
save_folder='tmp')
]
# Load the environment
env, state = OnlineFlexibleResourceAllocationEnv.load_env(
'training/settings/auction.env')
# Servers
server_1, server_2 = list(state.server_tasks.keys())
# Actions
actions = {server_1: 1.0, server_2: 2.0}
# Environment step
next_state, reward, done, info = env.step(actions)
# Server states
server_1_state = TaskPricingState(state.auction_task,
state.server_tasks[server_1], server_1,
state.time_step)
server_2_state = TaskPricingState(state.auction_task,
state.server_tasks[server_2], server_2,
state.time_step)
# Next server states
next_server_1_state = TaskPricingState(next_state.auction_task,
next_state.server_tasks[server_1],
server_1, next_state.time_step)
next_server_2_state = TaskPricingState(next_state.auction_task,
next_state.server_tasks[server_2],
server_2, next_state.time_step)
# Finished auction task
finished_task = next(finished_task
for finished_task in next_state.server_tasks[server_1]
if finished_task == state.auction_task)
finished_task = finished_task._replace(stage=TaskStage.COMPLETED)
failed_task = finished_task._replace(stage=TaskStage.FAILED)
# Loop over the agents, add the observations and try training
for agent in agents:
agent.winning_auction_bid(server_1_state, actions[server_1],
finished_task, next_server_1_state)
agent.winning_auction_bid(server_1_state, actions[server_1],
failed_task, next_server_1_state)
agent.failed_auction_bid(server_2_state, actions[server_2],
next_server_2_state)
agent.failed_auction_bid(server_2_state, 0, next_server_2_state)
agent.train()
print(
f'Rewards: {[trajectory[3] for trajectory in agents[0].replay_buffer]}'
)
def test_epsilon_policy():
print()
# Tests the epsilon policy by getting agent actions that should update the agent epsilon over time
env, state = OnlineFlexibleResourceAllocationEnv.load_env(
'agent/settings/actions.env')
# Number of epsilon steps for the agents
epsilon_steps = 25
# Agents that have a custom _get_action function
pricing_agents = [
TaskPricingDqnAgent(0,
create_lstm_dqn_network(9, 5),
epsilon_steps=epsilon_steps,
epsilon_update_freq=1,
epsilon_log_freq=1),
TaskPricingCategoricalDqnAgent(1,
create_lstm_categorical_dqn_network(
9, 5),
epsilon_steps=epsilon_steps,
epsilon_update_freq=1,
epsilon_log_freq=1),
TaskPricingDdpgAgent(2,
create_lstm_actor_network(9),
create_lstm_critic_network(9),
epsilon_steps=epsilon_steps,
epsilon_update_freq=1,
epsilon_log_freq=1)
]
weighting_agents = [
ResourceWeightingDqnAgent(0,
create_lstm_dqn_network(16, 5),
epsilon_steps=epsilon_steps,
epsilon_update_freq=1,
epsilon_log_freq=1),
ResourceWeightingCategoricalDqnAgent(
1,
create_lstm_categorical_dqn_network(16, 5),
epsilon_steps=epsilon_steps,
epsilon_update_freq=1,
epsilon_log_freq=1),
ResourceWeightingDdpgAgent(2,
create_lstm_actor_network(16),
create_lstm_critic_network(16),
epsilon_steps=epsilon_steps,
epsilon_update_freq=1,
epsilon_log_freq=1)
]
# Generate a tf writer and generate actions that will update the epsilon values for both agents
writer = tf.summary.create_file_writer(f'agent/tmp/testing_epsilon')
num_steps = 10
with writer.as_default():
for _ in range(num_steps):
for agent in pricing_agents:
actions = {
server: agent.bid(state.auction_task,
tasks,
server,
state.time_step,
training=True)
for server, tasks in state.server_tasks.items()
}
state, rewards, done, _ = env.step(actions)
for _ in range(num_steps):
for agent in weighting_agents:
actions = {
server: agent.weight(tasks,
server,
state.time_step,
training=True)
for server, tasks in state.server_tasks.items()
}
state, rewards, done, _ = env.step(actions)
# Check that the resulting total action are valid
for agent in pricing_agents:
print(f'Agent: {agent.name}')
assert agent.total_actions == num_steps * 3
for agent in weighting_agents:
print(f'Agent: {agent.name}')
assert agent.total_actions == num_steps * 3
# Check that the agent epsilon are correct
assert pricing_agents[0].final_epsilon == pricing_agents[
0].epsilon and pricing_agents[1].final_epsilon == pricing_agents[
1].epsilon
assert weighting_agents[0].final_epsilon == weighting_agents[
0].epsilon and weighting_agents[1].final_epsilon == weighting_agents[
1].epsilon
assert pricing_agents[2].final_epsilon_std == pricing_agents[2].epsilon_std
assert weighting_agents[2].final_epsilon_std == weighting_agents[
2].epsilon_std
def test_c51_actions():
print()
# Test the C51 agent actions
pricing_agent = TaskPricingCategoricalDqnAgent(
3, create_lstm_categorical_dqn_network(9, 5), initial_epsilon=0.5)
weighting_agent = ResourceWeightingCategoricalDqnAgent(
3, create_lstm_categorical_dqn_network(16, 5), initial_epsilon=0.5)
env, state = OnlineFlexibleResourceAllocationEnv.load_env(
'agent/settings/actions.env')
auction_actions = {
server: pricing_agent.bid(state.auction_task, tasks, server,
state.time_step)
for server, tasks in state.server_tasks.items()
}
print(f'Greedy actions: {list(auction_actions.values())}')
assert any(0 < action for server, action in auction_actions.items())
server, tasks = next(
(server, tasks) for server, tasks in state.server_tasks.items())
observation = tf.expand_dims(pricing_agent._network_obs(
state.auction_task, tasks, server, state.time_step),
axis=0)
network_output = pricing_agent.model_network(observation)
probabilities = tf.nn.softmax(network_output)
probability_value = probabilities * pricing_agent.z_values
q_values = tf.reduce_sum(probability_value, axis=2)
argmax_q_values = tf.math.argmax(q_values, axis=1, output_type=tf.int32)
print(
f'Network output: {network_output}\nProbabilities: {probabilities}\nProbability value: {probability_value}\n'
f'Q value: {q_values}\nArgmax Q value: {argmax_q_values}')
auction_actions = {
server: pricing_agent.bid(state.auction_task,
tasks,
server,
state.time_step,
training=True)
for server, tasks in state.server_tasks.items()
}
print(f'Epsilon Greedy actions: {list(auction_actions.values())}\n')
assert any(0 < action for server, action in auction_actions.items())
states, rewards, dones, _ = env.step(auction_actions)
weighting_actions = {
server: weighting_agent.weight(tasks, server, state.time_step)
for server, tasks in state.server_tasks.items()
}
print(
f'Greedy actions: {[list(actions.values()) for actions in weighting_actions.values()]}'
)
assert any(0 < action for server, action in auction_actions.items())
weighting_actions = {
server: weighting_agent.weight(tasks,
server,
state.time_step,
training=True)
for server, tasks in state.server_tasks.items()
}
print(
f'Greedy actions: {[list(actions.values()) for actions in weighting_actions.values()]}'
)
assert any(0 < action
for server, task_actions in weighting_actions.items()
for task, action in task_actions.items())
def test_build_agent():
def assert_args(test_agent, args):
"""
Asserts that the proposed arguments have assigned to the agent
Args:
test_agent: The test agent
args: The argument used on the agent
"""
for arg_name, arg_value in args.items():
assert getattr(test_agent, arg_name) == arg_value, \
f'Attr: {arg_name}, correct value: {arg_value}, actual value: {getattr(test_agent, arg_name)}'
# Check inheritance arguments
reinforcement_learning_arguments = {
'batch_size': 16,
'error_loss_fn': tf.compat.v1.losses.mean_squared_error,
'initial_training_replay_size': 1000,
'training_freq': 2,
'replay_buffer_length': 20000,
'save_frequency': 12500,
'save_folder': 'test',
'discount_factor': 0.9
}
dqn_arguments = {
'target_update_tau': 1.0,
'target_update_frequency': 2500,
'optimiser': tf.keras.optimizers.Adadelta(),
'initial_epsilon': 0.5,
'final_epsilon': 0.2,
'epsilon_update_freq': 25,
'epsilon_log_freq': 10,
}
ddpg_arguments = {
'actor_optimiser': tf.keras.optimizers.Adadelta(),
'critic_optimiser': tf.keras.optimizers.Adadelta(),
'initial_epsilon_std': 0.8,
'final_epsilon_std': 0.1,
'epsilon_update_freq': 25,
'epsilon_log_freq': 10,
'min_value': -15.0,
'max_value': 15
}
pricing_arguments = {
'failed_auction_reward': -100,
'failed_multiplier': -100
}
weighting_arguments = {
'other_task_discount': 0.2,
'success_reward': 1,
'failed_reward': -2
}
# DQN Agent arguments ----------------------------------------------------------------------
dqn_pricing_arguments = {
**reinforcement_learning_arguments,
**dqn_arguments,
**pricing_arguments
}
dqn_weighting_arguments = {
**reinforcement_learning_arguments,
**dqn_arguments,
**weighting_arguments
}
pricing_network = create_lstm_dqn_network(9, 10)
categorical_pricing_network = create_lstm_categorical_dqn_network(9, 10)
pricing_agents = [
TaskPricingDqnAgent(0, pricing_network, **dqn_pricing_arguments),
TaskPricingDdqnAgent(1, pricing_network, **dqn_pricing_arguments),
TaskPricingDuelingDqnAgent(2, pricing_network,
**dqn_pricing_arguments),
TaskPricingCategoricalDqnAgent(3, categorical_pricing_network,
**dqn_pricing_arguments)
]
for agent in pricing_agents:
print(f'Agent: {agent.name}')
assert_args(agent, dqn_pricing_arguments)
weighting_network = create_lstm_dqn_network(16, 10)
categorical_weighting_network = create_lstm_categorical_dqn_network(16, 10)
weighting_agents = [
ResourceWeightingDqnAgent(0, weighting_network,
**dqn_weighting_arguments),
ResourceWeightingDdqnAgent(1, weighting_network,
**dqn_weighting_arguments),
ResourceWeightingDuelingDqnAgent(2, weighting_network,
**dqn_weighting_arguments),
ResourceWeightingCategoricalDqnAgent(3, categorical_weighting_network,
**dqn_weighting_arguments)
]
for agent in weighting_agents:
print(f'Agent: {agent.name}')
assert_args(agent, dqn_weighting_arguments)
# PG Agent arguments ----------------------------------------------------------------------------------
ddpg_pricing_arguments = {
**reinforcement_learning_arguments,
**ddpg_arguments,
**pricing_arguments
}
ddpg_weighting_arguments = {
**reinforcement_learning_arguments,
**ddpg_arguments,
**weighting_arguments
}
pricing_agents = [
TaskPricingDdpgAgent(3, create_lstm_actor_network(9),
create_lstm_critic_network(9),
**ddpg_pricing_arguments),
TaskPricingTD3Agent(4, create_lstm_actor_network(9),
create_lstm_critic_network(9),
create_lstm_critic_network(9),
**ddpg_pricing_arguments)
]
for agent in pricing_agents:
print(f'Agent: {agent.name}')
assert_args(agent, ddpg_pricing_arguments)
weighting_agents = [
ResourceWeightingDdpgAgent(3, create_lstm_actor_network(16),
create_lstm_critic_network(16),
**ddpg_weighting_arguments),
ResourceWeightingTD3Agent(4, create_lstm_actor_network(16),
create_lstm_critic_network(16),
create_lstm_critic_network(16),
**ddpg_weighting_arguments)
]
for agent in weighting_agents:
print(f'Agent: {agent.name}')
assert_args(agent, ddpg_weighting_arguments)