def test_train(self):
norm_step = 1100
memory_init_size = 100
step_num = 1500
sess = tf.InteractiveSession()
tf.Variable(0, name='global_step', trainable=False)
agent = DQNAgent(sess=sess,
scope='dqn',
replay_memory_size = 500,
replay_memory_init_size=memory_init_size,
update_target_estimator_every=100,
norm_step=norm_step,
state_shape=[2],
mlp_layers=[10,10])
sess.run(tf.global_variables_initializer())
predicted_action = agent.eval_step({'obs': np.random.random_sample((2,)), 'legal_actions': [0, 1]})
self.assertGreaterEqual(predicted_action, 0)
self.assertLessEqual(predicted_action, 1)
for step in range(step_num):
ts = [{'obs': np.random.random_sample((2,)), 'legal_actions': [0, 1]}, np.random.randint(2), 0, {'obs': np.random.random_sample((2,)), 'legal_actions': [0, 1]}, True]
agent.feed(ts)
if step > norm_step + memory_init_size:
agent.train()
predicted_action = agent.step({'obs': np.random.random_sample((2,)), 'legal_actions': [0, 1]})
self.assertGreaterEqual(predicted_action, 0)
self.assertLessEqual(predicted_action, 1)
sess.close()
tf.reset_default_graph()
def test_train(self):
memory_init_size = 100
num_steps = 500
agent = DQNAgent(replay_memory_size = 200,
replay_memory_init_size=memory_init_size,
update_target_estimator_every=100,
state_shape=[2],
mlp_layers=[10,10],
device=torch.device('cpu'))
predicted_action, _ = agent.eval_step({'obs': np.random.random_sample((2,)), 'legal_actions': {0: None, 1: None}, 'raw_legal_actions': ['call', 'raise']})
self.assertGreaterEqual(predicted_action, 0)
self.assertLessEqual(predicted_action, 1)
for _ in range(num_steps):
ts = [{'obs': np.random.random_sample((2,)), 'legal_actions': {0: None, 1: None}}, np.random.randint(2), 0, {'obs': np.random.random_sample((2,)), 'legal_actions': {0: None, 1: None}, 'raw_legal_actions': ['call', 'raise']}, True]
agent.feed(ts)
predicted_action = agent.step({'obs': np.random.random_sample((2,)), 'legal_actions': {0: None, 1: None}})
self.assertGreaterEqual(predicted_action, 0)
self.assertLessEqual(predicted_action, 1)
scope='dqn',
action_num=env.action_num,
replay_memory_init_size=memory_init_size,
train_every=train_every,
state_shape=env.state_shape,
mlp_layers=[128, 128])
# Initialize global variables
sess.run(tf.global_variables_initializer())
# Init a Logger to plot the learning curve
logger = Logger(log_dir)
state = env.reset()
for timestep in range(timesteps):
action = agent.step(state)
next_state, reward, done = env.step(action)
ts = (state, action, reward, next_state, done)
agent.feed(ts)
if timestep % evaluate_every == 0:
rewards = []
state = eval_env.reset()
for _ in range(evaluate_num):
action, _ = agent.eval_step(state)
_, reward, done = env.step(action)
if done:
rewards.append(reward)
logger.log_performance(env.timestep, np.mean(rewards))
# Close files in the logger
class NFSPAgent(object):
''' An approximate clone of rlcard.agents.nfsp_agent that uses
pytorch instead of tensorflow. Note that this implementation
differs from Henrich and Silver (2016) in that the supervised
training minimizes cross-entropy with respect to the stored
action probabilities rather than the realized actions.
'''
def __init__(self,
num_actions=4,
state_shape=None,
hidden_layers_sizes=None,
reservoir_buffer_capacity=20000,
anticipatory_param=0.1,
batch_size=256,
train_every=1,
rl_learning_rate=0.1,
sl_learning_rate=0.005,
min_buffer_size_to_learn=100,
q_replay_memory_size=20000,
q_replay_memory_init_size=100,
q_update_target_estimator_every=1000,
q_discount_factor=0.99,
q_epsilon_start=0.06,
q_epsilon_end=0,
q_epsilon_decay_steps=int(1e6),
q_batch_size=32,
q_train_every=1,
q_mlp_layers=None,
evaluate_with='average_policy',
device=None):
''' Initialize the NFSP agent.
Args:
num_actions (int): The number of actions.
state_shape (list): The shape of the state space.
hidden_layers_sizes (list): The hidden layers sizes for the layers of
the average policy.
reservoir_buffer_capacity (int): The size of the buffer for average policy.
anticipatory_param (float): The hyper-parameter that balances rl/avarage policy.
batch_size (int): The batch_size for training average policy.
train_every (int): Train the SL policy every X steps.
rl_learning_rate (float): The learning rate of the RL agent.
sl_learning_rate (float): the learning rate of the average policy.
min_buffer_size_to_learn (int): The minimum buffer size to learn for average policy.
q_replay_memory_size (int): The memory size of inner DQN agent.
q_replay_memory_init_size (int): The initial memory size of inner DQN agent.
q_update_target_estimator_every (int): The frequency of updating target network for
inner DQN agent.
q_discount_factor (float): The discount factor of inner DQN agent.
q_epsilon_start (float): The starting epsilon of inner DQN agent.
q_epsilon_end (float): the end epsilon of inner DQN agent.
q_epsilon_decay_steps (int): The decay steps of inner DQN agent.
q_batch_size (int): The batch size of inner DQN agent.
q_train_step (int): Train the model every X steps.
q_mlp_layers (list): The layer sizes of inner DQN agent.
device (torch.device): Whether to use the cpu or gpu
'''
self.use_raw = False
self._num_actions = num_actions
self._state_shape = state_shape
self._layer_sizes = hidden_layers_sizes + [num_actions]
self._batch_size = batch_size
self._train_every = train_every
self._sl_learning_rate = sl_learning_rate
self._anticipatory_param = anticipatory_param
self._min_buffer_size_to_learn = min_buffer_size_to_learn
self._reservoir_buffer = ReservoirBuffer(reservoir_buffer_capacity)
self._prev_timestep = None
self._prev_action = None
self.evaluate_with = evaluate_with
if device is None:
self.device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
else:
self.device = device
# Total timesteps
self.total_t = 0
# Step counter to keep track of learning.
self._step_counter = 0
# Build the action-value network
self._rl_agent = DQNAgent(q_replay_memory_size, q_replay_memory_init_size, \
q_update_target_estimator_every, q_discount_factor, q_epsilon_start, q_epsilon_end, \
q_epsilon_decay_steps, q_batch_size, num_actions, state_shape, q_train_every, q_mlp_layers, \
rl_learning_rate, device)
# Build the average policy supervised model
self._build_model()
self.sample_episode_policy()
def _build_model(self):
''' Build the average policy network
'''
# configure the average policy network
policy_network = AveragePolicyNetwork(self._num_actions,
self._state_shape,
self._layer_sizes)
policy_network = policy_network.to(self.device)
self.policy_network = policy_network
self.policy_network.eval()
# xavier init
for p in self.policy_network.parameters():
if len(p.data.shape) > 1:
nn.init.xavier_uniform_(p.data)
# configure optimizer
self.policy_network_optimizer = torch.optim.Adam(
self.policy_network.parameters(), lr=self._sl_learning_rate)
def feed(self, ts):
''' Feed data to inner RL agent
Args:
ts (list): A list of 5 elements that represent the transition.
'''
self._rl_agent.feed(ts)
self.total_t += 1
if self.total_t > 0 and len(
self._reservoir_buffer
) >= self._min_buffer_size_to_learn and self.total_t % self._train_every == 0:
sl_loss = self.train_sl()
print('\rINFO - Step {}, sl-loss: {}'.format(
self.total_t, sl_loss),
end='')
def step(self, state):
''' Returns the action to be taken.
Args:
state (dict): The current state
Returns:
action (int): An action id
'''
obs = state['obs']
legal_actions = list(state['legal_actions'].keys())
if self._mode == 'best_response':
action = self._rl_agent.step(state)
one_hot = np.zeros(self._num_actions)
one_hot[action] = 1
self._add_transition(obs, one_hot)
elif self._mode == 'average_policy':
probs = self._act(obs)
probs = remove_illegal(probs, legal_actions)
action = np.random.choice(len(probs), p=probs)
return action
def eval_step(self, state):
''' Use the average policy for evaluation purpose
Args:
state (dict): The current state.
Returns:
action (int): An action id.
info (dict): A dictionary containing information
'''
if self.evaluate_with == 'best_response':
action, info = self._rl_agent.eval_step(state)
elif self.evaluate_with == 'average_policy':
obs = state['obs']
legal_actions = list(state['legal_actions'].keys())
probs = self._act(obs)
probs = remove_illegal(probs, legal_actions)
action = np.random.choice(len(probs), p=probs)
info = {}
info['probs'] = {
state['raw_legal_actions'][i]:
float(probs[list(state['legal_actions'].keys())[i]])
for i in range(len(state['legal_actions']))
}
else:
raise ValueError(
"'evaluate_with' should be either 'average_policy' or 'best_response'."
)
return action, info
def sample_episode_policy(self):
''' Sample average/best_response policy
'''
if np.random.rand() < self._anticipatory_param:
self._mode = 'best_response'
else:
self._mode = 'average_policy'
def _act(self, info_state):
''' Predict action probability givin the observation and legal actions
Not connected to computation graph
Args:
info_state (numpy.array): An obervation.
Returns:
action_probs (numpy.array): The predicted action probability.
'''
info_state = np.expand_dims(info_state, axis=0)
info_state = torch.from_numpy(info_state).float().to(self.device)
with torch.no_grad():
log_action_probs = self.policy_network(info_state).cpu().numpy()
action_probs = np.exp(log_action_probs)[0]
return action_probs
def _add_transition(self, state, probs):
''' Adds the new transition to the reservoir buffer.
Transitions are in the form (state, probs).
Args:
state (numpy.array): The state.
probs (numpy.array): The probabilities of each action.
'''
transition = Transition(info_state=state, action_probs=probs)
self._reservoir_buffer.add(transition)
def train_sl(self):
''' Compute the loss on sampled transitions and perform a avg-network update.
If there are not enough elements in the buffer, no loss is computed and
`None` is returned instead.
Returns:
loss (float): The average loss obtained on this batch of transitions or `None`.
'''
if (len(self._reservoir_buffer) < self._batch_size or
len(self._reservoir_buffer) < self._min_buffer_size_to_learn):
return None
transitions = self._reservoir_buffer.sample(self._batch_size)
info_states = [t.info_state for t in transitions]
action_probs = [t.action_probs for t in transitions]
self.policy_network_optimizer.zero_grad()
self.policy_network.train()
# (batch, state_size)
info_states = torch.from_numpy(np.array(info_states)).float().to(
self.device)
# (batch, num_actions)
eval_action_probs = torch.from_numpy(
np.array(action_probs)).float().to(self.device)
# (batch, num_actions)
log_forecast_action_probs = self.policy_network(info_states)
ce_loss = -(eval_action_probs *
log_forecast_action_probs).sum(dim=-1).mean()
ce_loss.backward()
self.policy_network_optimizer.step()
ce_loss = ce_loss.item()
self.policy_network.eval()
return ce_loss
def set_device(self, device):
self.device = device
self._rl_agent.set_device(device)
