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 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_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