File size: 14,277 Bytes
05c9ac2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 |
import pytest
from mlagents.torch_utils import torch
from mlagents.trainers.torch_entities.agent_action import AgentAction
from mlagents.trainers.torch_entities.networks import (
NetworkBody,
MultiAgentNetworkBody,
ValueNetwork,
SimpleActor,
SharedActorCritic,
)
from mlagents.trainers.settings import NetworkSettings
from mlagents_envs.base_env import ActionSpec
from mlagents.trainers.tests.dummy_config import create_observation_specs_with_shapes
def test_networkbody_vector():
torch.manual_seed(0)
obs_size = 4
network_settings = NetworkSettings()
obs_shapes = [(obs_size,)]
networkbody = NetworkBody(
create_observation_specs_with_shapes(obs_shapes),
network_settings,
encoded_act_size=2,
)
optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
sample_obs = 0.1 * torch.ones((1, obs_size))
sample_act = 0.1 * torch.ones((1, 2))
for _ in range(300):
encoded, _ = networkbody([sample_obs], sample_act)
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_networkbody_lstm():
torch.manual_seed(0)
obs_size = 4
seq_len = 6
network_settings = NetworkSettings(
memory=NetworkSettings.MemorySettings(sequence_length=seq_len, memory_size=12)
)
obs_shapes = [(obs_size,)]
networkbody = NetworkBody(
create_observation_specs_with_shapes(obs_shapes), network_settings
)
optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-4)
sample_obs = torch.ones((seq_len, obs_size))
for _ in range(300):
encoded, _ = networkbody(
[sample_obs], 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 test_networkbody_visual():
torch.manual_seed(1)
vec_obs_size = 4
obs_size = (84, 84, 3)
network_settings = NetworkSettings()
obs_shapes = [(vec_obs_size,), obs_size]
networkbody = NetworkBody(
create_observation_specs_with_shapes(obs_shapes), network_settings
)
optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
sample_obs = 0.1 * torch.ones((1, 84, 84, 3), dtype=torch.float32)
sample_vec_obs = torch.ones((1, vec_obs_size), dtype=torch.float32)
obs = [sample_vec_obs] + [sample_obs]
loss = 1
step = 0
while loss > 1e-6 and step < 1e3:
encoded, _ = networkbody(obs)
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()
step += 1
# 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)
@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
def test_multinetworkbody_vector(with_actions):
torch.manual_seed(0)
obs_size = 4
act_size = 2
n_agents = 3
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
)
optimizer = torch.optim.Adam(networkbody.parameters(), lr=3e-3)
sample_obs = [[0.1 * torch.ones((1, obs_size))] 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=[])
# 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)
@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
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)
@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
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=[])
# 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_valuenetwork():
torch.manual_seed(0)
obs_size = 4
num_outputs = 2
network_settings = NetworkSettings()
obs_spec = create_observation_specs_with_shapes([(obs_size,)])
stream_names = [f"stream_name{n}" for n in range(4)]
value_net = ValueNetwork(
stream_names, obs_spec, network_settings, outputs_per_stream=num_outputs
)
optimizer = torch.optim.Adam(value_net.parameters(), lr=3e-3)
for _ in range(50):
sample_obs = torch.ones((1, obs_size))
values, _ = value_net([sample_obs])
loss = 0
for s_name in stream_names:
assert values[s_name].shape == (1, num_outputs)
# Try to force output to 1
loss += torch.nn.functional.mse_loss(
values[s_name], torch.ones((1, num_outputs))
)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# In the last step, values should be close to 1
for value in values.values():
for _out in value.tolist():
assert _out[0] == pytest.approx(1.0, abs=0.1)
@pytest.mark.parametrize("shared", [True, False])
@pytest.mark.parametrize("lstm", [True, False])
def test_actor_critic(lstm, shared):
obs_size = 4
vis_obs_size = (84, 84, 3)
network_settings = NetworkSettings(
memory=NetworkSettings.MemorySettings() if lstm else None, normalize=True
)
obs_spec = create_observation_specs_with_shapes([(obs_size,), vis_obs_size])
act_size = 2
mask = torch.ones([1, act_size * 2])
stream_names = [f"stream_name{n}" for n in range(4)]
action_spec = ActionSpec(act_size, tuple(act_size for _ in range(act_size)))
if shared:
actor = critic = SharedActorCritic(
obs_spec, network_settings, action_spec, stream_names, network_settings
)
else:
actor = SimpleActor(obs_spec, network_settings, action_spec)
critic = ValueNetwork(stream_names, obs_spec, network_settings)
if lstm:
sample_vis_obs = torch.ones(
(network_settings.memory.sequence_length, 84, 84, 3), dtype=torch.float32
)
sample_obs = torch.ones((network_settings.memory.sequence_length, obs_size))
memories = torch.ones(
(1, network_settings.memory.sequence_length, actor.memory_size)
)
else:
sample_vis_obs = 0.1 * torch.ones((1, 84, 84, 3), dtype=torch.float32)
sample_obs = torch.ones((1, obs_size))
memories = torch.tensor([])
# memories isn't always set to None, the network should be able to
# deal with that.
# Test critic pass
value_out, memories_out = critic.critic_pass(
[sample_obs] + [sample_vis_obs], memories=memories
)
for stream in stream_names:
if lstm:
assert value_out[stream].shape == (network_settings.memory.sequence_length,)
assert memories_out.shape == memories.shape
else:
assert value_out[stream].shape == (1,)
# Test get action stats and_value
action, run_out, mem_out = actor.get_action_and_stats(
[sample_obs] + [sample_vis_obs], memories=memories, masks=mask
)
log_probs = run_out["log_probs"]
entropy = run_out["entropy"]
eval_run_out = actor.get_stats(
[sample_obs] + [sample_vis_obs], action, memories=memories, masks=mask
)
eval_log_probs = eval_run_out["log_probs"]
eval_entropy = eval_run_out["entropy"]
if lstm:
assert action.continuous_tensor.shape == (64, 2)
assert log_probs.continuous_tensor.shape == (64, 2)
assert entropy.shape == (64,)
assert eval_log_probs.continuous_tensor.shape == (64, 2)
assert eval_entropy.shape == (64,)
else:
assert action.continuous_tensor.shape == (1, 2)
assert log_probs.continuous_tensor.shape == (1, 2)
assert entropy.shape == (1,)
assert eval_log_probs.continuous_tensor.shape == (1, 2)
assert eval_entropy.shape == (1,)
assert len(action.discrete_list) == 2
for _disc, _disc_prob, _eval_disc_prob in zip(
action.discrete_list, log_probs.discrete_list, eval_log_probs.discrete_list
):
if lstm:
assert _disc.shape == (64, 1)
assert _eval_disc_prob.shape == (64,)
else:
assert _disc.shape == (1, 1)
assert _disc_prob.shape == (1,)
assert _eval_disc_prob.shape == (1,)
if mem_out is not None:
assert mem_out.shape == memories.shape
@pytest.mark.parametrize("with_actions", [True, False], ids=["actions", "no_actions"])
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
|