def test_ppo_get_state():
# Assign
state_size, action_size = 3, 4
init_config = {'actor_lr': 0.1, 'gamma': 0.6}
agent = PPOAgent(state_size, action_size, device='cpu', **init_config)
# Act
agent_state = agent.get_state()
# Assert
assert isinstance(agent_state, AgentState)
assert agent_state.model == PPOAgent.name
assert agent_state.state_space == state_size
assert agent_state.action_space == action_size
assert agent_state.config == agent._config
assert agent_state.config['actor_lr'] == 0.1
assert agent_state.config['gamma'] == 0.6
network_state = agent_state.network
assert isinstance(network_state, NetworkState)
assert {'actor', 'critic', 'policy'} == set(network_state.net.keys())
buffer_state = agent_state.buffer
assert isinstance(buffer_state, BufferState)
assert buffer_state.type == agent.buffer.type
assert buffer_state.batch_size == agent.buffer.batch_size
assert buffer_state.buffer_size == agent.buffer.buffer_size
def test_ppo_from_state():
# Assign
state_shape, action_size = 10, 3
agent = PPOAgent(state_shape, action_size)
agent_state = agent.get_state()
# Act
new_agent = PPOAgent.from_state(agent_state)
# Assert
assert id(agent) != id(new_agent)
# assert new_agent == agent
assert isinstance(new_agent, PPOAgent)
assert new_agent.hparams == agent.hparams
assert all([
torch.all(x == y) for (
x,
y) in zip(agent.policy.parameters(), new_agent.policy.parameters())
])
assert all([
torch.all(x == y)
for (x,
y) in zip(agent.actor.parameters(), new_agent.actor.parameters())
])
assert all([
torch.all(x == y) for (
x,
y) in zip(agent.critic.parameters(), new_agent.critic.parameters())
])
assert new_agent.buffer == agent.buffer
def test_ppo_from_state_one_updated():
# Assign
state_shape, action_size = 10, 3
agent = PPOAgent(state_shape, action_size)
deterministic_interactions(agent, num_iters=100)
agent_state = agent.get_state()
deterministic_interactions(agent, num_iters=400)
# Act
new_agent = PPOAgent.from_state(agent_state)
# Assert
assert id(agent) != id(new_agent)
# assert new_agent == agent
assert isinstance(new_agent, PPOAgent)
# assert any([torch.any(x != y) for (x, y) in zip(agent.policy.parameters(), new_agent.policy.parameters())])
assert any([
torch.any(x != y)
for (x,
y) in zip(agent.actor.parameters(), new_agent.actor.parameters())
])
assert any([
torch.any(x != y) for (
x,
y) in zip(agent.critic.parameters(), new_agent.critic.parameters())
])
assert new_agent.buffer != agent.buffer
def test_agent_factory_ppo_agent_from_state_network_buffer_none():
# Assign
state_size, action_size = 10, 5
agent = PPOAgent(state_size, action_size, device="cpu")
state = agent.get_state()
state.network = None
state.buffer = None
# Act
new_agent = AgentFactory.from_state(state)
# Assert
assert id(new_agent) != id(agent)
assert new_agent.hparams == agent.hparams
def test_agent_factory_ppo_agent_from_state():
# Assign
state_size, action_size = 10, 5
agent = PPOAgent(state_size, action_size, device="cpu")
state = agent.get_state()
# Act
new_agent = AgentFactory.from_state(state)
# Assert
assert id(new_agent) != id(agent)
assert new_agent == agent
assert new_agent.name == PPOAgent.name
assert new_agent.hparams == agent.hparams
assert new_agent.buffer == agent.buffer
def test_ppo_from_state_network_state_none():
# Assign
state_shape, action_size = 10, 3
agent = PPOAgent(state_shape, action_size)
agent_state = agent.get_state()
agent_state.network = None
# Act
new_agent = PPOAgent.from_state(agent_state)
# Assert
assert id(agent) != id(new_agent)
# assert new_agent == agent
assert isinstance(new_agent, PPOAgent)
assert new_agent.hparams == agent.hparams
assert new_agent.buffer == agent.buffer
def test_runs_ppo():
# Assign
task = GymTask('Pendulum-v0')
agent = PPOAgent(task.state_size, task.action_size, device=DEVICE)
env_runner = EnvRunner(task, agent, max_iterations=50)
# Act
env_runner.run(reward_goal=10, max_episodes=10, force_new=True)
def from_state(state: AgentState) -> AgentBase:
if state.model == DQNAgent.name:
return DQNAgent.from_state(state)
elif state.model == PPOAgent.name:
return PPOAgent.from_state(state)
else:
raise ValueError(
f"Agent state contains unsupported model type: '{state.model}'"
)
def from_state(state: AgentState) -> AgentBase:
norm_model = state.model.upper()
if norm_model == DQNAgent.name.upper():
return DQNAgent.from_state(state)
elif norm_model == PPOAgent.name.upper():
return PPOAgent.from_state(state)
elif norm_model == DDPGAgent.name.upper():
return DDPGAgent.from_state(state)
elif norm_model == RainbowAgent.name.upper():
return RainbowAgent.from_state(state)
else:
raise ValueError(
f"Agent state contains unsupported model type: {state.model}")
def test_multi_sync_env_runner_run_single_step_multiple_task():
# Assign
tasks: List[TaskType] = [test_task, test_task]
agent = PPOAgent(test_task.state_size,
test_task.action_size,
num_workers=len(tasks))
multi_sync_env_runner = MultiSyncEnvRunner(tasks, agent)
# Act
scores = multi_sync_env_runner.run(max_episodes=1,
max_iterations=1,
force_new=True)
# Assert
assert len(
scores) == 2 # After 1 iteration both "finished" at the same time
def test_multi_sync_env_runner_run_multiple_step_multiple_task():
# Assign
tasks: List[TaskType] = [test_task, test_task]
agent = PPOAgent(test_task.state_size,
test_task.action_size,
num_workers=len(tasks))
multi_sync_env_runner = MultiSyncEnvRunner(tasks, agent)
# Act
scores = multi_sync_env_runner.run(max_episodes=3,
max_iterations=100,
force_new=True)
# Assert
assert len(scores) in (
3, 4
) # On rare occasions two tasks can complete twice at the same time.
def test_ppo_get_state_compare_different_agents():
# Assign
state_size, action_size = 3, 2
agent_1 = PPOAgent(state_size, action_size, device='cpu', n_steps=1)
agent_2 = PPOAgent(state_size, action_size, device='cpu', n_steps=2)
# Act
state_1 = agent_1.get_state()
state_2 = agent_2.get_state()
# Assert
assert state_1 != state_2
assert state_1.model == state_2.model
def test_ppo_seed():
# Assign
agent_0 = PPOAgent(4, 2, device='cpu') # Reference
agent_1 = PPOAgent(4, 2, device='cpu')
agent_2 = copy.deepcopy(agent_1)
# Act
# Make sure agents have the same networks
assert any([
sum(sum(l1.weight - l2.weight)) != 0
for l1, l2 in zip(agent_0.actor.layers, agent_1.actor.layers)
])
assert all([
sum(sum(l1.weight - l2.weight)) == 0
for l1, l2 in zip(agent_1.actor.layers, agent_2.actor.layers)
])
assert all([
sum(sum(l1.weight - l2.weight)) == 0
for l1, l2 in zip(agent_1.critic.layers, agent_2.critic.layers)
])
agent_0.seed(32167)
actions_0 = deterministic_interactions(agent_0)
agent_1.seed(0)
actions_1 = deterministic_interactions(agent_1)
agent_2.seed(0)
actions_2 = deterministic_interactions(agent_2)
# Assert
# First we check that there's definitely more than one type of action
assert actions_1[0] != actions_1[1]
assert actions_2[0] != actions_2[1]
# All generated actions need to identical
assert any(a0 != a1 for (a0, a1) in zip(actions_0, actions_1))
for idx, (a1, a2) in enumerate(zip(actions_1, actions_2)):
assert a1 == pytest.approx(
a2, 1e-4), f"Action mismatch on position {idx}: {a1} != {a2}"
import mock
import random
from ai_traineree.agents.ppo import PPOAgent
from ai_traineree.env_runner import EnvRunner, MultiSyncEnvRunner
from ai_traineree.tasks import GymTask
from ai_traineree.types import TaskType
from typing import List
# NOTE: Some of these tests use `test_task` and `test_agent` which are real instances.
# This is partially to make sure that the tricky part is covered, and not hid
# by aggressive mocking. The other part, however, is the burden of keeping env mocks.
# This results in unnecessary performance hit. A lightweight env would be nice.
test_task = GymTask('LunarLanderContinuous-v2')
test_agent = PPOAgent(test_task.state_size, test_task.action_size)
@mock.patch("ai_traineree.env_runner.AgentBase")
@mock.patch("ai_traineree.env_runner.TaskType")
def test_env_runner_info_no_data_logger(mock_task, mock_agent):
# Assign
env_runner = EnvRunner(mock_task, mock_agent)
env_runner.logger = mock.MagicMock()
info_data = dict(episodes=[2],
iterations=[10],
scores=[1],
mean_scores=[2],
epsilons=[1])
# Act
