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import numpy as np
import pytest
import torch
from easydict import EasyDict
from lzero.policy import inverse_scalar_transform, select_action
policy = 'GumbelMuZero'
if policy == 'EfficientZero':
from lzero.mcts.tree_search.mcts_ctree import EfficientZeroMCTSCtree as MCTSCtree
elif policy == 'GumbelMuZero':
from lzero.mcts.tree_search.mcts_ctree import GumbelMuZeroMCTSCtree as MCTSCtree
else:
raise KeyError('Only support test for EfficientZero and GumbelMuZero.')
class MuZeroModelFake(torch.nn.Module):
"""
Overview:
Fake MuZero model just for test EfficientZeroMCTSCtree and GumbelMuZeroMCTSCtree.
Interfaces:
__init__, initial_inference, recurrent_inference
"""
def __init__(self, action_num):
super().__init__()
self.action_num = action_num
def initial_inference(self, observation):
encoded_state = observation
batch_size = encoded_state.shape[0]
value = torch.zeros(size=(batch_size, 601))
value_prefix = [0. for _ in range(batch_size)]
policy_logits = torch.zeros(size=(batch_size, self.action_num))
latent_state = torch.zeros(size=(batch_size, 12, 3, 3))
reward_hidden_state_roots = (torch.zeros(size=(1, batch_size, 16)), torch.zeros(size=(1, batch_size, 16)))
output = {
'value': value,
'value_prefix': value_prefix,
'policy_logits': policy_logits,
'latent_state': latent_state,
'reward_hidden_state': reward_hidden_state_roots
}
if policy == 'GumbelMuZero':
output['reward'] = value_prefix
return EasyDict(output)
def recurrent_inference(self, latent_states, reward_hidden_states, actions=None):
if policy == 'GumbelMuZero':
assert actions == None
actions = reward_hidden_states
batch_size = latent_states.shape[0]
latent_state = torch.zeros(size=(batch_size, 12, 3, 3))
reward_hidden_state_roots = (torch.zeros(size=(1, batch_size, 16)), torch.zeros(size=(1, batch_size, 16)))
value = torch.zeros(size=(batch_size, 601))
value_prefix = torch.zeros(size=(batch_size, 601))
policy_logits = torch.zeros(size=(batch_size, self.action_num))
output = {
'value': value,
'value_prefix': value_prefix,
'policy_logits': policy_logits,
'latent_state': latent_state,
'reward_hidden_state': reward_hidden_state_roots
}
if policy == 'GumbelMuZero':
output['reward'] = value_prefix
return EasyDict(output)
policy_config = EasyDict(
lstm_horizon_len=5,
num_simulations=8,
batch_size=16,
pb_c_base=1,
pb_c_init=1,
max_num_considered_actions=6,
discount_factor=0.9,
root_dirichlet_alpha=0.3,
root_noise_weight=0.2,
dirichlet_alpha=0.3,
exploration_fraction=1,
device='cpu',
value_delta_max=0.01,
model=dict(
action_space_size=9,
support_scale=300,
categorical_distribution=True,
),
)
batch_size = env_nums = policy_config.batch_size
action_space_size = policy_config.model.action_space_size
model = MuZeroModelFake(action_num=9)
stack_obs = torch.zeros(size=(batch_size, 8), dtype=torch.float)
network_output = model.initial_inference(stack_obs.float())
latent_state_roots = network_output['latent_state']
reward_hidden_state_roots = network_output['reward_hidden_state']
pred_values_pool = network_output['value']
value_prefix_pool = network_output['value_prefix']
policy_logits_pool = network_output['policy_logits']
# network output process
pred_values_pool = inverse_scalar_transform(pred_values_pool, policy_config.model.support_scale).detach().cpu().numpy()
latent_state_roots = latent_state_roots.detach().cpu().numpy()
reward_hidden_state_roots = (
reward_hidden_state_roots[0].detach().cpu().numpy(), reward_hidden_state_roots[1].detach().cpu().numpy()
)
policy_logits_pool = policy_logits_pool.detach().cpu().numpy().tolist()
action_mask = [
[0, 0, 0, 1, 0, 1, 1, 0, 0],
[1, 0, 0, 1, 0, 0, 1, 0, 0],
[1, 1, 0, 0, 1, 0, 1, 0, 1],
[1, 0, 0, 1, 1, 1, 0, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 1],
[0, 1, 1, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 1, 1, 0, 0, 1, 1],
[1, 1, 1, 1, 1, 0, 0, 0, 1],
[0, 0, 0, 1, 0, 1, 1, 0, 0],
[0, 1, 1, 0, 1, 1, 1, 1, 0],
[1, 1, 1, 0, 0, 0, 1, 1, 1],
[1, 1, 0, 1, 0, 1, 1, 0, 0],
[0, 0, 1, 0, 0, 1, 0, 0, 0],
[1, 0, 1, 1, 0, 0, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 1, 0, 0, 1],
]
assert len(action_mask) == batch_size
assert len(action_mask[0]) == action_space_size
action_num = [
int(np.array(action_mask[i]).sum()) for i in range(env_nums)
] # [3, 3, 5, 4, 3, 3, 6, 6, 3, 6, 6, 5, 2, 5, 1, 4]
legal_actions_list = [[i for i, x in enumerate(action_mask[j]) if x == 1] for j in range(env_nums)]
# legal_actions_list =
# [[3, 5, 6], [0, 3, 6], [0, 1, 4, 6, 8], [0, 3, 4, 5],
# [2, 5, 8], [1, 2, 4], [0, 2, 3, 4, 7, 8], [0, 1, 2, 3, 4, 8],
# [3, 5, 6], [1, 2, 4, 5, 6, 7], [0, 1, 2, 6, 7, 8], [0, 1, 3, 5, 6],
# [2, 5], [0, 2, 3, 6, 7], [1], [0, 4, 5, 8]]
to_play = [2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 1, 1]
assert len(to_play) == batch_size
@pytest.mark.unittest
def test_mcts_vs_bot_to_play():
legal_actions_list = [[i for i in range(action_space_size)] for _ in range(env_nums)] # all action
roots = MCTSCtree.roots(env_nums, legal_actions_list)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * policy_config.model.action_space_size
).astype(np.float32).tolist() for _ in range(env_nums)
]
# In ctree, to_play must be list, not None
if policy == 'EfficientZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, [0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, reward_hidden_state_roots, [0 for _ in range(env_nums)])
elif policy == 'GumbelMuZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, list(pred_values_pool), policy_logits_pool,
[0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, [0 for _ in range(env_nums)])
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert np.array(roots_distributions).shape == (batch_size, action_space_size)
assert np.array(roots_values).shape == (batch_size,)
@pytest.mark.unittest
def test_mcts_vs_bot_to_play_large():
policy_config.obs_space_size = 100
policy_config.model.action_space_size = 20
policy_config.num_simulations = 500
policy_config.batch_size = 256
env_nums = policy_config.batch_size
model = MuZeroModelFake(action_num=policy_config.model.action_space_size)
# stack_obs = torch.zeros(size=(policy_config.batch_size, policy_config.obs_space_size), dtype=torch.float)
stack_obs = torch.randn(size=(policy_config.batch_size, policy_config.obs_space_size), dtype=torch.float)
network_output = model.initial_inference(stack_obs.float())
latent_state_roots = network_output['latent_state']
reward_hidden_state_roots = network_output['reward_hidden_state']
pred_values_pool = network_output['value']
value_prefix_pool = network_output['value_prefix']
policy_logits_pool = network_output['policy_logits']
# network output process
pred_values_pool = inverse_scalar_transform(pred_values_pool,
policy_config.model.support_scale).detach().cpu().numpy()
latent_state_roots = latent_state_roots.detach().cpu().numpy()
reward_hidden_state_roots = (
reward_hidden_state_roots[0].detach().cpu().numpy(), reward_hidden_state_roots[1].detach().cpu().numpy()
)
policy_logits_pool = policy_logits_pool.detach().cpu().numpy().tolist()
# all actions are legal
legal_actions_list = [[i for i in range(policy_config.model.action_space_size)] for _ in range(env_nums)]
roots = MCTSCtree.roots(env_nums, legal_actions_list)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * policy_config.model.action_space_size
).astype(np.float32).tolist() for _ in range(env_nums)
]
# In ctree, to_play must be list, not None
if policy == 'EfficientZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, [0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, reward_hidden_state_roots, [0 for _ in range(env_nums)])
elif policy == 'GumbelMuZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, list(pred_values_pool), policy_logits_pool,
[0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, [0 for _ in range(env_nums)])
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert np.array(roots_distributions).shape == (policy_config.batch_size, policy_config.model.action_space_size)
assert np.array(roots_values).shape == (policy_config.batch_size,)
@pytest.mark.unittest
def test_mcts_vs_bot_to_play_legal_action():
for i in range(env_nums):
assert action_num[i] == len(legal_actions_list[i])
roots = MCTSCtree.roots(env_nums, legal_actions_list)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * int(sum(action_mask[j]))).astype(np.float32).tolist()
for j in range(env_nums)
]
# In ctree, to_play must be list, not None
if policy == 'EfficientZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, [0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, reward_hidden_state_roots, [0 for _ in range(env_nums)])
elif policy == 'GumbelMuZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, list(pred_values_pool), policy_logits_pool,
[0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, [0 for _ in range(env_nums)])
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert len(roots_values) == env_nums
assert len(roots_values) == env_nums
for i in range(env_nums):
assert len(roots_distributions[i]) == action_num[i]
temperature = [1 for _ in range(env_nums)]
for i in range(env_nums):
distributions = roots_distributions[i]
action_index, visit_count_distribution_entropy = select_action(
distributions, temperature=temperature[i], deterministic=False
)
action = np.where(np.array(action_mask[i]) == 1.0)[0][action_index]
assert action_index < action_num[i]
assert action == legal_actions_list[i][action_index]
print('\n action_index={}, legal_action={}, action={}'.format(action_index, legal_actions_list[i], action))
@pytest.mark.unittest
def test_mcts_self_play():
legal_actions_list = [[i for i in range(action_space_size)] for _ in range(env_nums)] # all action
roots = MCTSCtree.roots(env_nums, legal_actions_list)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * policy_config.model.action_space_size
).astype(np.float32).tolist() for _ in range(env_nums)
]
# In ctree, to_play must be list, not None
if policy == 'EfficientZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, [0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, reward_hidden_state_roots, [0 for _ in range(env_nums)])
elif policy == 'GumbelMuZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, list(pred_values_pool), policy_logits_pool,
[0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, [0 for _ in range(env_nums)])
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert np.array(roots_distributions).shape == (batch_size, action_space_size)
assert np.array(roots_values).shape == (batch_size,)
@pytest.mark.unittest
def test_mcts_self_play_legal_action():
for i in range(env_nums):
assert action_num[i] == len(legal_actions_list[i])
roots = MCTSCtree.roots(env_nums, legal_actions_list)
noises = [
np.random.dirichlet([policy_config.root_dirichlet_alpha] * int(sum(action_mask[j]))).astype(np.float32).tolist()
for j in range(env_nums)
]
# In ctree, to_play must be list, not None
if policy == 'EfficientZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, policy_logits_pool, [0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, reward_hidden_state_roots, [0 for _ in range(env_nums)])
elif policy == 'GumbelMuZero':
roots.prepare(
policy_config.root_noise_weight, noises, value_prefix_pool, list(pred_values_pool), policy_logits_pool,
[0 for _ in range(env_nums)]
)
MCTSCtree(policy_config
).search(roots, model, latent_state_roots, [0 for _ in range(env_nums)])
roots_distributions = roots.get_distributions()
roots_values = roots.get_values()
assert len(roots_values) == env_nums
assert len(roots_values) == env_nums
for i in range(env_nums):
assert len(roots_distributions[i]) == action_num[i]
temperature = [1 for _ in range(env_nums)]
for i in range(env_nums):
distributions = roots_distributions[i]
action_index, visit_count_distribution_entropy = select_action(
distributions, temperature=temperature[i], deterministic=False
)
action = np.where(np.array(action_mask[i]) == 1.0)[0][action_index]
assert action_index < action_num[i]
assert action == legal_actions_list[i][action_index]
print('\n action_index={}, legal_action={}, action={}'.format(action_index, legal_actions_list[i], action))
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