def test_slice():
# Both continuous and discrete
aa = AgentAction(
torch.tensor([[1.0], [1.0], [1.0]]),
[torch.tensor([2, 1, 0]), torch.tensor([1, 2, 0])],
)
saa = aa.slice(0, 2)
assert saa.continuous_tensor.shape == (2, 1)
assert saa.discrete_tensor.shape == (2, 2)
def test_agent_action_group_from_buffer():
buff = AgentBuffer()
# Create some actions
for _ in range(3):
buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
3 * [np.ones((5,), dtype=np.float32)]
)
buff[BufferKey.GROUP_DISCRETE_ACTION].append(
3 * [np.ones((4,), dtype=np.float32)]
)
# Some agents have died
for _ in range(2):
buff[BufferKey.GROUP_CONTINUOUS_ACTION].append(
1 * [np.ones((5,), dtype=np.float32)]
)
buff[BufferKey.GROUP_DISCRETE_ACTION].append(
1 * [np.ones((4,), dtype=np.float32)]
)
# Get the group actions, which will be a List of Lists of AgentAction, where each element is the same
# length as the AgentBuffer but contains only one agent's obs. Dead agents are padded by
# NaNs.
gact = AgentAction.group_from_buffer(buff)
# Agent 0 is full
agent_0_act = gact[0]
assert agent_0_act.continuous_tensor.shape == (buff.num_experiences, 5)
assert agent_0_act.discrete_tensor.shape == (buff.num_experiences, 4)
agent_1_act = gact[1]
assert agent_1_act.continuous_tensor.shape == (buff.num_experiences, 5)
assert agent_1_act.discrete_tensor.shape == (buff.num_experiences, 4)
assert (agent_1_act.continuous_tensor[0:3] > 0).all()
assert (agent_1_act.continuous_tensor[3:] == 0).all()
assert (agent_1_act.discrete_tensor[0:3] > 0).all()
assert (agent_1_act.discrete_tensor[3:] == 0).all()
def test_multinetworkbody_num_agents(with_actions):
torch.manual_seed(0)
act_size = 2
obs_size = 4
network_settings = NetworkSettings()
obs_shapes = [(obs_size,)]
action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
networkbody = MultiAgentNetworkBody(
create_observation_specs_with_shapes(obs_shapes), network_settings, action_spec
)
sample_obs = [[0.1 * torch.ones((1, obs_size))]]
# simulate baseline in POCA
sample_act = [
AgentAction(
0.1 * torch.ones((1, 2)), [0.1 * torch.ones(1) for _ in range(act_size)]
)
]
for n_agent, max_so_far in [(1, 1), (5, 5), (4, 5), (10, 10), (5, 10), (1, 10)]:
if with_actions:
encoded, _ = networkbody(
obs_only=sample_obs * (n_agent - 1), obs=sample_obs, actions=sample_act
)
else:
encoded, _ = networkbody(obs_only=sample_obs * n_agent, obs=[], actions=[])
# look at the last value of the hidden units (the number of agents)
target = (n_agent * 1.0 / max_so_far) * 2 - 1
assert abs(encoded[0, -1].item() - target) < 1e-6
assert encoded[0, -1].item() <= 1
assert encoded[0, -1].item() >= -1
def test_evaluate_actions(rnn, visual, discrete):
policy = create_policy_mock(
TrainerSettings(), use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
buffer = mb.simulate_rollout(64, policy.behavior_spec, memory_size=policy.m_size)
act_masks = ModelUtils.list_to_tensor(buffer[BufferKey.ACTION_MASK])
agent_action = AgentAction.from_buffer(buffer)
np_obs = ObsUtil.from_buffer(buffer, len(policy.behavior_spec.observation_specs))
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
memories = [
ModelUtils.list_to_tensor(buffer[BufferKey.MEMORY][i])
for i in range(0, len(buffer[BufferKey.MEMORY]), policy.sequence_length)
]
if len(memories) > 0:
memories = torch.stack(memories).unsqueeze(0)
log_probs, entropy, values = policy.evaluate_actions(
tensor_obs,
masks=act_masks,
actions=agent_action,
memories=memories,
seq_len=policy.sequence_length,
)
if discrete:
_size = policy.behavior_spec.action_spec.discrete_size
else:
_size = policy.behavior_spec.action_spec.continuous_size
assert log_probs.flatten().shape == (64, _size)
assert entropy.shape == (64,)
for val in values.values():
assert val.shape == (64,)
def _sample_action(self, dists: DistInstances) -> AgentAction:
"""
Samples actions from a DistInstances tuple
:params dists: The DistInstances tuple
:return: An AgentAction corresponding to the actions sampled from the DistInstances
"""
continuous_action: Optional[torch.Tensor] = None
discrete_action: Optional[List[torch.Tensor]] = None
# This checks None because mypy complains otherwise
if dists.continuous is not None:
if self._deterministic:
continuous_action = dists.continuous.deterministic_sample()
else:
continuous_action = dists.continuous.sample()
if dists.discrete is not None:
discrete_action = []
if self._deterministic:
for discrete_dist in dists.discrete:
discrete_action.append(
discrete_dist.deterministic_sample())
else:
for discrete_dist in dists.discrete:
discrete_action.append(discrete_dist.sample())
return AgentAction(continuous_action, discrete_action)
def get_action_input(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Creates the action Tensor. In continuous case, corresponds to the action. In
the discrete case, corresponds to the concatenation of one hot action Tensors.
"""
return self._action_flattener.forward(
AgentAction.from_dict(mini_batch))
def predict_next_state(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Uses the current state embedding and the action of the mini_batch to predict
the next state embedding.
"""
actions = AgentAction.from_buffer(mini_batch)
flattened_action = self._action_flattener.forward(actions)
forward_model_input = torch.cat(
(self.get_current_state(mini_batch), flattened_action), dim=1)
return self.forward_model_next_state_prediction(forward_model_input)
def test_to_flat():
# Both continuous and discrete
aa = AgentAction(torch.tensor([[1.0, 1.0, 1.0]]),
[torch.tensor([2]), torch.tensor([1])])
flattened_actions = aa.to_flat([3, 3])
assert torch.eq(flattened_actions,
torch.tensor([[1, 1, 1, 0, 0, 1, 0, 1, 0]])).all()
# Just continuous
aa = AgentAction(torch.tensor([[1.0, 1.0, 1.0]]), None)
flattened_actions = aa.to_flat([])
assert torch.eq(flattened_actions, torch.tensor([1, 1, 1])).all()
# Just discrete
aa = AgentAction(torch.tensor([]), [torch.tensor([2]), torch.tensor([1])])
flattened_actions = aa.to_flat([3, 3])
assert torch.eq(flattened_actions, torch.tensor([0, 0, 1, 0, 1, 0])).all()
def test_multinetworkbody_lstm(with_actions):
torch.manual_seed(0)
obs_size = 4
act_size = 2
seq_len = 16
n_agents = 3
network_settings = NetworkSettings(memory=NetworkSettings.MemorySettings(
sequence_length=seq_len, memory_size=12))
obs_shapes = [(obs_size, )]
action_spec = ActionSpec(act_size,
tuple(act_size for _ in range(act_size)))
networkbody = MultiAgentNetworkBody(
create_observation_specs_with_shapes(obs_shapes), network_settings,
action_spec)
optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-4)
sample_obs = [[0.1 * torch.ones((seq_len, obs_size))]
for _ in range(n_agents)]
# simulate baseline in POCA
sample_act = [
AgentAction(
0.1 * torch.ones((seq_len, 2)),
[0.1 * torch.ones(seq_len) for _ in range(act_size)],
) for _ in range(n_agents - 1)
]
for _ in range(300):
if with_actions:
encoded, _ = networkbody(
obs_only=sample_obs[:1],
obs=sample_obs[1:],
actions=sample_act,
memories=torch.ones(1, 1, 12),
sequence_length=seq_len,
)
else:
encoded, _ = networkbody(
obs_only=sample_obs,
obs=[],
actions=[],
memories=torch.ones(1, 1, 12),
sequence_length=seq_len,
)
# Try to force output to 1
loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# In the last step, values should be close to 1
for _enc in encoded.flatten().tolist():
assert _enc == pytest.approx(1.0, abs=0.1)
def _update_batch(self, mini_batch_demo: Dict[str, np.ndarray],
n_sequences: int) -> Dict[str, float]:
"""
Helper function for update_batch.
"""
vec_obs = [ModelUtils.list_to_tensor(mini_batch_demo["vector_obs"])]
act_masks = None
expert_actions = AgentAction.from_dict(mini_batch_demo)
if self.policy.behavior_spec.action_spec.discrete_size > 0:
act_masks = ModelUtils.list_to_tensor(
np.ones(
(
self.n_sequences * self.policy.sequence_length,
sum(self.policy.behavior_spec.action_spec.
discrete_branches),
),
dtype=np.float32,
))
memories = []
if self.policy.use_recurrent:
memories = torch.zeros(1, self.n_sequences, self.policy.m_size)
if self.policy.use_vis_obs:
vis_obs = []
for idx, _ in enumerate(
self.policy.actor_critic.network_body.visual_processors):
vis_ob = ModelUtils.list_to_tensor(
mini_batch_demo["visual_obs%d" % idx])
vis_obs.append(vis_ob)
else:
vis_obs = []
selected_actions, log_probs, _, _ = self.policy.sample_actions(
vec_obs,
vis_obs,
masks=act_masks,
memories=memories,
seq_len=self.policy.sequence_length,
)
bc_loss = self._behavioral_cloning_loss(selected_actions, log_probs,
expert_actions)
self.optimizer.zero_grad()
bc_loss.backward()
self.optimizer.step()
run_out = {"loss": bc_loss.item()}
return run_out
def _update_batch(
self, mini_batch_demo: AgentBuffer, n_sequences: int
) -> Dict[str, float]:
"""
Helper function for update_batch.
"""
np_obs = ObsUtil.from_buffer(
mini_batch_demo, len(self.policy.behavior_spec.observation_specs)
)
# Convert to tensors
tensor_obs = [ModelUtils.list_to_tensor(obs) for obs in np_obs]
act_masks = None
expert_actions = AgentAction.from_buffer(mini_batch_demo)
if self.policy.behavior_spec.action_spec.discrete_size > 0:
act_masks = ModelUtils.list_to_tensor(
np.ones(
(
self.n_sequences * self.policy.sequence_length,
sum(self.policy.behavior_spec.action_spec.discrete_branches),
),
dtype=np.float32,
)
)
memories = []
if self.policy.use_recurrent:
memories = torch.zeros(1, self.n_sequences, self.policy.m_size)
selected_actions, log_probs, _, _ = self.policy.sample_actions(
tensor_obs,
masks=act_masks,
memories=memories,
seq_len=self.policy.sequence_length,
)
bc_loss = self._behavioral_cloning_loss(
selected_actions, log_probs, expert_actions
)
self.optimizer.zero_grad()
bc_loss.backward()
self.optimizer.step()
run_out = {"loss": bc_loss.item()}
return run_out
def test_evaluate_actions(rnn, visual, discrete):
policy = create_policy_mock(TrainerSettings(),
use_rnn=rnn,
use_discrete=discrete,
use_visual=visual)
buffer = mb.simulate_rollout(64,
policy.behavior_spec,
memory_size=policy.m_size)
vec_obs = [ModelUtils.list_to_tensor(buffer["vector_obs"])]
act_masks = ModelUtils.list_to_tensor(buffer["action_mask"])
agent_action = AgentAction.from_dict(buffer)
vis_obs = []
for idx, _ in enumerate(
policy.actor_critic.network_body.visual_processors):
vis_ob = ModelUtils.list_to_tensor(buffer["visual_obs%d" % idx])
vis_obs.append(vis_ob)
memories = [
ModelUtils.list_to_tensor(buffer["memory"][i])
for i in range(0, len(buffer["memory"]), policy.sequence_length)
]
if len(memories) > 0:
memories = torch.stack(memories).unsqueeze(0)
log_probs, entropy, values = policy.evaluate_actions(
vec_obs,
vis_obs,
masks=act_masks,
actions=agent_action,
memories=memories,
seq_len=policy.sequence_length,
)
if discrete:
_size = policy.behavior_spec.action_spec.discrete_size
else:
_size = policy.behavior_spec.action_spec.continuous_size
assert log_probs.flatten().shape == (64, _size)
assert entropy.shape == (64, )
for val in values.values():
assert val.shape == (64, )
def compute_inverse_loss(self, mini_batch: AgentBuffer) -> torch.Tensor:
"""
Computes the inverse loss for a mini_batch. Corresponds to the error on the
action prediction (given the current and next state).
"""
predicted_action = self.predict_action(mini_batch)
actions = AgentAction.from_dict(mini_batch)
_inverse_loss = 0
if self._action_spec.continuous_size > 0:
sq_difference = (
actions.continuous_tensor - predicted_action.continuous
) ** 2
sq_difference = torch.sum(sq_difference, dim=1)
_inverse_loss += torch.mean(
ModelUtils.dynamic_partition(
sq_difference,
ModelUtils.list_to_tensor(mini_batch["masks"], dtype=torch.float),
2,
)[1]
)
if self._action_spec.discrete_size > 0:
true_action = torch.cat(
ModelUtils.actions_to_onehot(
actions.discrete_tensor, self._action_spec.discrete_branches
),
dim=1,
)
cross_entropy = torch.sum(
-torch.log(predicted_action.discrete + self.EPSILON) * true_action,
dim=1,
)
_inverse_loss += torch.mean(
ModelUtils.dynamic_partition(
cross_entropy,
ModelUtils.list_to_tensor(
mini_batch["masks"], dtype=torch.float
), # use masks not action_masks
2,
)[1]
)
return _inverse_loss
def test_multinetworkbody_visual(with_actions):
torch.manual_seed(0)
act_size = 2
n_agents = 3
obs_size = 4
vis_obs_size = (84, 84, 3)
network_settings = NetworkSettings()
obs_shapes = [(obs_size, ), vis_obs_size]
action_spec = ActionSpec(act_size,
tuple(act_size for _ in range(act_size)))
networkbody = MultiAgentNetworkBody(
create_observation_specs_with_shapes(obs_shapes), network_settings,
action_spec)
optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
sample_obs = [[0.1 * torch.ones(
(1, obs_size))] + [0.1 * torch.ones((1, 84, 84, 3))]
for _ in range(n_agents)]
# simulate baseline in POCA
sample_act = [
AgentAction(0.1 * torch.ones((1, 2)),
[0.1 * torch.ones(1) for _ in range(act_size)])
for _ in range(n_agents - 1)
]
for _ in range(300):
if with_actions:
encoded, _ = networkbody(obs_only=sample_obs[:1],
obs=sample_obs[1:],
actions=sample_act)
else:
encoded, _ = networkbody(obs_only=sample_obs, obs=[], actions=[])
assert encoded.shape == (1, network_settings.hidden_units)
# Try to force output to 1
loss = torch.nn.functional.mse_loss(encoded, torch.ones(encoded.shape))
optimizer.zero_grad()
loss.backward()
optimizer.step()
# In the last step, values should be close to 1
for _enc in encoded.flatten().tolist():
assert _enc == pytest.approx(1.0, abs=0.1)
def test_get_probs_and_entropy():
inp_size = 4
act_size = 2
action_model, masks = create_action_model(inp_size, act_size)
_continuous_dist = GaussianDistInstance(torch.zeros((1, 2)),
torch.ones((1, 2)))
act_size = 2
test_prob = torch.tensor([[1.0 - 0.1 * (act_size - 1)] + [0.1] *
(act_size - 1)])
_discrete_dist_list = [
CategoricalDistInstance(test_prob),
CategoricalDistInstance(test_prob),
]
dist_tuple = DistInstances(_continuous_dist, _discrete_dist_list)
agent_action = AgentAction(torch.zeros(
(1, 2)), [torch.tensor([0]), torch.tensor([1])])
log_probs, entropies = action_model._get_probs_and_entropy(
agent_action, dist_tuple)
assert log_probs.continuous_tensor.shape == (1, 2)
assert len(log_probs.discrete_list) == 2
for _disc in log_probs.discrete_list:
assert _disc.shape == (1, )
assert len(log_probs.all_discrete_list) == 2
for _disc in log_probs.all_discrete_list:
assert _disc.shape == (1, 2)
for clp in log_probs.continuous_tensor[0]:
# Log prob of standard normal at 0
assert clp == pytest.approx(-0.919, abs=0.01)
assert log_probs.discrete_list[0] > log_probs.discrete_list[1]
for ent, val in zip(entropies[0], [1.4189, 0.6191, 0.6191]):
assert ent == pytest.approx(val, abs=0.01)
def update(self, batch: AgentBuffer,
num_sequences: int) -> Dict[str, float]:
"""
Performs update on model.
:param batch: Batch of experiences.
:param num_sequences: Number of sequences to process.
:return: Results of update.
"""
# Get decayed parameters
decay_lr = self.decay_learning_rate.get_value(
self.policy.get_current_step())
decay_eps = self.decay_epsilon.get_value(
self.policy.get_current_step())
decay_bet = self.decay_beta.get_value(self.policy.get_current_step())
returns = {}
old_values = {}
for name in self.reward_signals:
old_values[name] = ModelUtils.list_to_tensor(
batch[f"{name}_value_estimates"])
returns[name] = ModelUtils.list_to_tensor(batch[f"{name}_returns"])
vec_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
act_masks = ModelUtils.list_to_tensor(batch["action_mask"])
actions = AgentAction.from_dict(batch)
memories = [
ModelUtils.list_to_tensor(batch["memory"][i]) for i in range(
0, len(batch["memory"]), self.policy.sequence_length)
]
if len(memories) > 0:
memories = torch.stack(memories).unsqueeze(0)
if self.policy.use_vis_obs:
vis_obs = []
for idx, _ in enumerate(
self.policy.actor_critic.network_body.visual_processors):
vis_ob = ModelUtils.list_to_tensor(batch["visual_obs%d" % idx])
vis_obs.append(vis_ob)
else:
vis_obs = []
log_probs, entropy, values = self.policy.evaluate_actions(
vec_obs,
vis_obs,
masks=act_masks,
actions=actions,
memories=memories,
seq_len=self.policy.sequence_length,
)
old_log_probs = ActionLogProbs.from_dict(batch).flatten()
log_probs = log_probs.flatten()
loss_masks = ModelUtils.list_to_tensor(batch["masks"],
dtype=torch.bool)
value_loss = self.ppo_value_loss(values, old_values, returns,
decay_eps, loss_masks)
policy_loss = self.ppo_policy_loss(
ModelUtils.list_to_tensor(batch["advantages"]),
log_probs,
old_log_probs,
loss_masks,
)
loss = (policy_loss + 0.5 * value_loss -
decay_bet * ModelUtils.masked_mean(entropy, loss_masks))
# Set optimizer learning rate
ModelUtils.update_learning_rate(self.optimizer, decay_lr)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
update_stats = {
# NOTE: abs() is not technically correct, but matches the behavior in TensorFlow.
# TODO: After PyTorch is default, change to something more correct.
"Losses/Policy Loss": torch.abs(policy_loss).item(),
"Losses/Value Loss": value_loss.item(),
"Policy/Learning Rate": decay_lr,
"Policy/Epsilon": decay_eps,
"Policy/Beta": decay_bet,
}
for reward_provider in self.reward_signals.values():
update_stats.update(reward_provider.update(batch))
return update_stats
def update(self, batch: AgentBuffer,
num_sequences: int) -> Dict[str, float]:
"""
Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param batch: Experience mini-batch.
:param update_target: Whether or not to update target value network
:param reward_signal_batches: Minibatches to use for updating the reward signals,
indexed by name. If none, don't update the reward signals.
:return: Output from update process.
"""
rewards = {}
for name in self.reward_signals:
rewards[name] = ModelUtils.list_to_tensor(
batch[RewardSignalUtil.rewards_key(name)])
n_obs = len(self.policy.behavior_spec.observation_specs)
current_obs = ObsUtil.from_buffer(batch, n_obs)
# Convert to tensors
current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
next_obs = ObsUtil.from_buffer_next(batch, n_obs)
# Convert to tensors
next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
act_masks = ModelUtils.list_to_tensor(batch[BufferKey.ACTION_MASK])
actions = AgentAction.from_buffer(batch)
memories_list = [
ModelUtils.list_to_tensor(batch[BufferKey.MEMORY][i]) for i in
range(0, len(batch[BufferKey.MEMORY]), self.policy.sequence_length)
]
# LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
value_memories_list = [
ModelUtils.list_to_tensor(batch[BufferKey.CRITIC_MEMORY][i])
for i in range(0, len(batch[BufferKey.CRITIC_MEMORY]),
self.policy.sequence_length)
]
if len(memories_list) > 0:
memories = torch.stack(memories_list).unsqueeze(0)
value_memories = torch.stack(value_memories_list).unsqueeze(0)
else:
memories = None
value_memories = None
# Q and V network memories are 0'ed out, since we don't have them during inference.
q_memories = (torch.zeros_like(value_memories)
if value_memories is not None else None)
# Copy normalizers from policy
self.q_network.q1_network.network_body.copy_normalization(
self.policy.actor.network_body)
self.q_network.q2_network.network_body.copy_normalization(
self.policy.actor.network_body)
self.target_network.network_body.copy_normalization(
self.policy.actor.network_body)
self._critic.network_body.copy_normalization(
self.policy.actor.network_body)
sampled_actions, log_probs, _, _, = self.policy.actor.get_action_and_stats(
current_obs,
masks=act_masks,
memories=memories,
sequence_length=self.policy.sequence_length,
)
value_estimates, _ = self._critic.critic_pass(
current_obs,
value_memories,
sequence_length=self.policy.sequence_length)
cont_sampled_actions = sampled_actions.continuous_tensor
cont_actions = actions.continuous_tensor
q1p_out, q2p_out = self.q_network(
current_obs,
cont_sampled_actions,
memories=q_memories,
sequence_length=self.policy.sequence_length,
q2_grad=False,
)
q1_out, q2_out = self.q_network(
current_obs,
cont_actions,
memories=q_memories,
sequence_length=self.policy.sequence_length,
)
if self._action_spec.discrete_size > 0:
disc_actions = actions.discrete_tensor
q1_stream = self._condense_q_streams(q1_out, disc_actions)
q2_stream = self._condense_q_streams(q2_out, disc_actions)
else:
q1_stream, q2_stream = q1_out, q2_out
with torch.no_grad():
# Since we didn't record the next value memories, evaluate one step in the critic to
# get them.
if value_memories is not None:
# Get the first observation in each sequence
just_first_obs = [
_obs[::self.policy.sequence_length] for _obs in current_obs
]
_, next_value_memories = self._critic.critic_pass(
just_first_obs, value_memories, sequence_length=1)
else:
next_value_memories = None
target_values, _ = self.target_network(
next_obs,
memories=next_value_memories,
sequence_length=self.policy.sequence_length,
)
masks = ModelUtils.list_to_tensor(batch[BufferKey.MASKS],
dtype=torch.bool)
dones = ModelUtils.list_to_tensor(batch[BufferKey.DONE])
q1_loss, q2_loss = self.sac_q_loss(q1_stream, q2_stream, target_values,
dones, rewards, masks)
value_loss = self.sac_value_loss(log_probs, value_estimates, q1p_out,
q2p_out, masks)
policy_loss = self.sac_policy_loss(log_probs, q1p_out, masks)
entropy_loss = self.sac_entropy_loss(log_probs, masks)
total_value_loss = q1_loss + q2_loss
if self.policy.shared_critic:
policy_loss += value_loss
else:
total_value_loss += value_loss
decay_lr = self.decay_learning_rate.get_value(
self.policy.get_current_step())
ModelUtils.update_learning_rate(self.policy_optimizer, decay_lr)
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
ModelUtils.update_learning_rate(self.value_optimizer, decay_lr)
self.value_optimizer.zero_grad()
total_value_loss.backward()
self.value_optimizer.step()
ModelUtils.update_learning_rate(self.entropy_optimizer, decay_lr)
self.entropy_optimizer.zero_grad()
entropy_loss.backward()
self.entropy_optimizer.step()
# Update target network
ModelUtils.soft_update(self._critic, self.target_network, self.tau)
update_stats = {
"Losses/Policy Loss":
policy_loss.item(),
"Losses/Value Loss":
value_loss.item(),
"Losses/Q1 Loss":
q1_loss.item(),
"Losses/Q2 Loss":
q2_loss.item(),
"Policy/Discrete Entropy Coeff":
torch.mean(torch.exp(self._log_ent_coef.discrete)).item(),
"Policy/Continuous Entropy Coeff":
torch.mean(torch.exp(self._log_ent_coef.continuous)).item(),
"Policy/Learning Rate":
decay_lr,
}
return update_stats
def get_trajectory_and_baseline_value_estimates(
self,
batch: AgentBuffer,
next_obs: List[np.ndarray],
next_groupmate_obs: List[List[np.ndarray]],
done: bool,
agent_id: str = "",
) -> Tuple[Dict[str, np.ndarray], Dict[str, np.ndarray], Dict[str, float],
Optional[AgentBufferField], Optional[AgentBufferField], ]:
"""
Get value estimates, baseline estimates, and memories for a trajectory, in batch form.
:param batch: An AgentBuffer that consists of a trajectory.
:param next_obs: the next observation (after the trajectory). Used for boostrapping
if this is not a termiinal trajectory.
:param next_groupmate_obs: the next observations from other members of the group.
:param done: Set true if this is a terminal trajectory.
:param agent_id: Agent ID of the agent that this trajectory belongs to.
:returns: A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)],
the baseline estimates as a Dict, the final value estimate as a Dict of [name, float], and
optionally (if using memories) an AgentBufferField of initial critic and baseline memories to be used
during update.
"""
n_obs = len(self.policy.behavior_spec.observation_specs)
current_obs = ObsUtil.from_buffer(batch, n_obs)
groupmate_obs = GroupObsUtil.from_buffer(batch, n_obs)
current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
groupmate_obs = [[
ModelUtils.list_to_tensor(obs) for obs in _groupmate_obs
] for _groupmate_obs in groupmate_obs]
groupmate_actions = AgentAction.group_from_buffer(batch)
next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
next_obs = [obs.unsqueeze(0) for obs in next_obs]
next_groupmate_obs = [
ModelUtils.list_to_tensor_list(_list_obs)
for _list_obs in next_groupmate_obs
]
# Expand dimensions of next critic obs
next_groupmate_obs = [[_obs.unsqueeze(0) for _obs in _list_obs]
for _list_obs in next_groupmate_obs]
if agent_id in self.value_memory_dict:
# The agent_id should always be in both since they are added together
_init_value_mem = self.value_memory_dict[agent_id]
_init_baseline_mem = self.baseline_memory_dict[agent_id]
else:
_init_value_mem = (torch.zeros((1, 1, self.critic.memory_size))
if self.policy.use_recurrent else None)
_init_baseline_mem = (torch.zeros((1, 1, self.critic.memory_size))
if self.policy.use_recurrent else None)
all_obs = ([current_obs] + groupmate_obs
if groupmate_obs is not None else [current_obs])
all_next_value_mem: Optional[AgentBufferField] = None
all_next_baseline_mem: Optional[AgentBufferField] = None
with torch.no_grad():
if self.policy.use_recurrent:
(
value_estimates,
baseline_estimates,
all_next_value_mem,
all_next_baseline_mem,
next_value_mem,
next_baseline_mem,
) = self._evaluate_by_sequence_team(
current_obs,
groupmate_obs,
groupmate_actions,
_init_value_mem,
_init_baseline_mem,
)
else:
value_estimates, next_value_mem = self.critic.critic_pass(
all_obs,
_init_value_mem,
sequence_length=batch.num_experiences)
groupmate_obs_and_actions = (groupmate_obs, groupmate_actions)
baseline_estimates, next_baseline_mem = self.critic.baseline(
current_obs,
groupmate_obs_and_actions,
_init_baseline_mem,
sequence_length=batch.num_experiences,
)
# Store the memory for the next trajectory
self.value_memory_dict[agent_id] = next_value_mem
self.baseline_memory_dict[agent_id] = next_baseline_mem
all_next_obs = ([next_obs] + next_groupmate_obs
if next_groupmate_obs is not None else [next_obs])
next_value_estimates, _ = self.critic.critic_pass(all_next_obs,
next_value_mem,
sequence_length=1)
for name, estimate in baseline_estimates.items():
baseline_estimates[name] = ModelUtils.to_numpy(estimate)
for name, estimate in value_estimates.items():
value_estimates[name] = ModelUtils.to_numpy(estimate)
# the base line and V shpuld not be on the same done flag
for name, estimate in next_value_estimates.items():
next_value_estimates[name] = ModelUtils.to_numpy(estimate)
if done:
for k in next_value_estimates:
if not self.reward_signals[k].ignore_done:
next_value_estimates[k][-1] = 0.0
return (
value_estimates,
baseline_estimates,
next_value_estimates,
all_next_value_mem,
all_next_baseline_mem,
)
def update(self, batch: AgentBuffer,
num_sequences: int) -> Dict[str, float]:
"""
Performs update on model.
:param batch: Batch of experiences.
:param num_sequences: Number of sequences to process.
:return: Results of update.
"""
# Get decayed parameters
decay_lr = self.decay_learning_rate.get_value(
self.policy.get_current_step())
decay_eps = self.decay_epsilon.get_value(
self.policy.get_current_step())
decay_bet = self.decay_beta.get_value(self.policy.get_current_step())
returns = {}
old_values = {}
for name in self.reward_signals:
old_values[name] = ModelUtils.list_to_tensor(
batch[RewardSignalUtil.value_estimates_key(name)])
returns[name] = ModelUtils.list_to_tensor(
batch[RewardSignalUtil.returns_key(name)])
n_obs = len(self.policy.behavior_spec.observation_specs)
current_obs = ObsUtil.from_buffer(batch, n_obs)
# Convert to tensors
current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
act_masks = ModelUtils.list_to_tensor(batch[BufferKey.ACTION_MASK])
actions = AgentAction.from_buffer(batch)
memories = [
ModelUtils.list_to_tensor(batch[BufferKey.MEMORY][i]) for i in
range(0, len(batch[BufferKey.MEMORY]), self.policy.sequence_length)
]
if len(memories) > 0:
memories = torch.stack(memories).unsqueeze(0)
# Get value memories
value_memories = [
ModelUtils.list_to_tensor(batch[BufferKey.CRITIC_MEMORY][i])
for i in range(0, len(batch[BufferKey.CRITIC_MEMORY]),
self.policy.sequence_length)
]
if len(value_memories) > 0:
value_memories = torch.stack(value_memories).unsqueeze(0)
log_probs, entropy = self.policy.evaluate_actions(
current_obs,
masks=act_masks,
actions=actions,
memories=memories,
seq_len=self.policy.sequence_length,
)
values, _ = self.critic.critic_pass(
current_obs,
memories=value_memories,
sequence_length=self.policy.sequence_length,
)
old_log_probs = ActionLogProbs.from_buffer(batch).flatten()
log_probs = log_probs.flatten()
loss_masks = ModelUtils.list_to_tensor(batch[BufferKey.MASKS],
dtype=torch.bool)
value_loss = self.ppo_value_loss(values, old_values, returns,
decay_eps, loss_masks)
policy_loss = self.ppo_policy_loss(
ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]),
log_probs,
old_log_probs,
loss_masks,
)
loss = (policy_loss + 0.5 * value_loss -
decay_bet * ModelUtils.masked_mean(entropy, loss_masks))
# Set optimizer learning rate
ModelUtils.update_learning_rate(self.optimizer, decay_lr)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
update_stats = {
# NOTE: abs() is not technically correct, but matches the behavior in TensorFlow.
# TODO: After PyTorch is default, change to something more correct.
"Losses/Policy Loss": torch.abs(policy_loss).item(),
"Losses/Value Loss": value_loss.item(),
"Policy/Learning Rate": decay_lr,
"Policy/Epsilon": decay_eps,
"Policy/Beta": decay_bet,
}
for reward_provider in self.reward_signals.values():
update_stats.update(reward_provider.update(batch))
return update_stats
def update(self, batch: AgentBuffer,
num_sequences: int) -> Dict[str, float]:
"""
Updates model using buffer.
:param num_sequences: Number of trajectories in batch.
:param batch: Experience mini-batch.
:param update_target: Whether or not to update target value network
:param reward_signal_batches: Minibatches to use for updating the reward signals,
indexed by name. If none, don't update the reward signals.
:return: Output from update process.
"""
rewards = {}
for name in self.reward_signals:
rewards[name] = ModelUtils.list_to_tensor(batch[f"{name}_rewards"])
n_obs = len(self.policy.behavior_spec.sensor_specs)
current_obs = ObsUtil.from_buffer(batch, n_obs)
# Convert to tensors
current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
next_obs = ObsUtil.from_buffer_next(batch, n_obs)
# Convert to tensors
next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
act_masks = ModelUtils.list_to_tensor(batch["action_mask"])
actions = AgentAction.from_dict(batch)
memories_list = [
ModelUtils.list_to_tensor(batch["memory"][i]) for i in range(
0, len(batch["memory"]), self.policy.sequence_length)
]
# LSTM shouldn't have sequence length <1, but stop it from going out of the index if true.
offset = 1 if self.policy.sequence_length > 1 else 0
next_memories_list = [
ModelUtils.list_to_tensor(
batch["memory"][i]
[self.policy.m_size //
2:]) # only pass value part of memory to target network
for i in range(offset, len(batch["memory"]),
self.policy.sequence_length)
]
if len(memories_list) > 0:
memories = torch.stack(memories_list).unsqueeze(0)
next_memories = torch.stack(next_memories_list).unsqueeze(0)
else:
memories = None
next_memories = None
# Q network memories are 0'ed out, since we don't have them during inference.
q_memories = (torch.zeros_like(next_memories)
if next_memories is not None else None)
# Copy normalizers from policy
self.value_network.q1_network.network_body.copy_normalization(
self.policy.actor_critic.network_body)
self.value_network.q2_network.network_body.copy_normalization(
self.policy.actor_critic.network_body)
self.target_network.network_body.copy_normalization(
self.policy.actor_critic.network_body)
(
sampled_actions,
log_probs,
_,
value_estimates,
_,
) = self.policy.actor_critic.get_action_stats_and_value(
current_obs,
masks=act_masks,
memories=memories,
sequence_length=self.policy.sequence_length,
)
cont_sampled_actions = sampled_actions.continuous_tensor
cont_actions = actions.continuous_tensor
q1p_out, q2p_out = self.value_network(
current_obs,
cont_sampled_actions,
memories=q_memories,
sequence_length=self.policy.sequence_length,
q2_grad=False,
)
q1_out, q2_out = self.value_network(
current_obs,
cont_actions,
memories=q_memories,
sequence_length=self.policy.sequence_length,
)
if self._action_spec.discrete_size > 0:
disc_actions = actions.discrete_tensor
q1_stream = self._condense_q_streams(q1_out, disc_actions)
q2_stream = self._condense_q_streams(q2_out, disc_actions)
else:
q1_stream, q2_stream = q1_out, q2_out
with torch.no_grad():
target_values, _ = self.target_network(
next_obs,
memories=next_memories,
sequence_length=self.policy.sequence_length,
)
masks = ModelUtils.list_to_tensor(batch["masks"], dtype=torch.bool)
dones = ModelUtils.list_to_tensor(batch["done"])
q1_loss, q2_loss = self.sac_q_loss(q1_stream, q2_stream, target_values,
dones, rewards, masks)
value_loss = self.sac_value_loss(log_probs, value_estimates, q1p_out,
q2p_out, masks)
policy_loss = self.sac_policy_loss(log_probs, q1p_out, masks)
entropy_loss = self.sac_entropy_loss(log_probs, masks)
total_value_loss = q1_loss + q2_loss + value_loss
decay_lr = self.decay_learning_rate.get_value(
self.policy.get_current_step())
ModelUtils.update_learning_rate(self.policy_optimizer, decay_lr)
self.policy_optimizer.zero_grad()
policy_loss.backward()
self.policy_optimizer.step()
ModelUtils.update_learning_rate(self.value_optimizer, decay_lr)
self.value_optimizer.zero_grad()
total_value_loss.backward()
self.value_optimizer.step()
ModelUtils.update_learning_rate(self.entropy_optimizer, decay_lr)
self.entropy_optimizer.zero_grad()
entropy_loss.backward()
self.entropy_optimizer.step()
# Update target network
ModelUtils.soft_update(self.policy.actor_critic.critic,
self.target_network, self.tau)
update_stats = {
"Losses/Policy Loss":
policy_loss.item(),
"Losses/Value Loss":
value_loss.item(),
"Losses/Q1 Loss":
q1_loss.item(),
"Losses/Q2 Loss":
q2_loss.item(),
"Policy/Discrete Entropy Coeff":
torch.mean(torch.exp(self._log_ent_coef.discrete)).item(),
"Policy/Continuous Entropy Coeff":
torch.mean(torch.exp(self._log_ent_coef.continuous)).item(),
"Policy/Learning Rate":
decay_lr,
}
return update_stats
