def test_init(self):
sess = tf.InteractiveSession()
env = rlcard.make('leduc-holdem', allow_step_back=True)
agent = DeepCFR(session=sess,
env=env,
policy_network_layers=(4,4),
advantage_network_layers=(4,4),
num_traversals=1,
num_step=1,
learning_rate=1e-4,
batch_size_advantage=10,
batch_size_strategy=10,
memory_capacity=int(1e7))
self.assertEqual(agent._num_traversals, 1)
self.assertEqual(agent._num_step, 1)
self.assertEqual(agent._batch_size_advantage, 10)
self.assertEqual(agent._batch_size_strategy, 10)
sess.close()
tf.reset_default_graph()
def test_train(self):
num_iterations = 10
sess = tf.InteractiveSession()
env = rlcard.make('leduc-holdem', {'allow_step_back': True})
agent = DeepCFR(session=sess,
scope='deepcfr',
env=env,
policy_network_layers=(128, 128),
advantage_network_layers=(128, 128),
num_traversals=1,
num_step=1,
learning_rate=1e-4,
batch_size_advantage=64,
batch_size_strategy=64,
memory_capacity=int(1e5))
# Test train
for _ in range(num_iterations):
agent.train()
# Test eval_step
state = {
'obs': np.random.random_sample(env.state_shape),
'legal_actions': [a for a in range(env.action_num)]
}
action, _ = agent.eval_step(state)
self.assertIn(action, [a for a in range(env.action_num)])
# Test simulate other
action = agent.simulate_other(0, state)
self.assertIn(action, [a for a in range(env.action_num)])
# Test action advantage
advantages = agent.action_advantage(state, 0)
self.assertEqual(advantages.shape[0], env.action_num)
sess.close()
tf.reset_default_graph()