def test_ddpg_seed():
# Assign
agent_0 = DDPGAgent(4, 2, device='cpu')
agent_1 = DDPGAgent(4, 2, device='cpu')
agent_2 = copy.deepcopy(agent_1)
# Act
# Make sure agents have the same networks
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}"
def test_rainbow_seed():
# Assign
agent_0 = RainbowAgent(4, 4, device='cpu')
agent_1 = RainbowAgent(4, 4, device='cpu')
agent_2 = copy.deepcopy(agent_1)
# Act
# Make sure agents have the same networks
agent_nets = zip(agent_1.net.value_net.layers,
agent_2.net.value_net.layers)
agent_target_nets = zip(agent_1.target_net.value_net.layers,
agent_2.target_net.value_net.layers)
assert all([sum(sum(l1.weight - l2.weight)) == 0 for l1, l2 in agent_nets])
assert all(
[sum(sum(l1.weight - l2.weight)) == 0 for l1, l2 in agent_target_nets])
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
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 == a2, f"Action mismatch on position {idx}: {a1} != {a2}"
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_serialize_network_state_actual():
from ai_traineree.agents.dqn import DQNAgent
agent = DQNAgent(10, 4)
deterministic_interactions(agent, 30)
network_state = agent.get_network_state()
# Act
ser = serialize(network_state)
# Assert
des = json.loads(ser)
assert set(des['net'].keys()) == set(('target_net', 'net'))
def test_serialize_agent_state_actual():
from ai_traineree.agents.dqn import DQNAgent
agent = DQNAgent(10, 4)
deterministic_interactions(agent, 30)
state = agent.get_state()
# Act
ser = serialize(state)
# Assert
des = json.loads(ser)
assert des['model'] == DQNAgent.name
assert len(des['buffer']['data']) == 30
assert set(des['network']['net'].keys()) == set(('target_net', 'net'))
