def test_train(self):
memory_init_size = 20
num_steps = 1000
agent = NFSPAgent(num_actions=2,
state_shape=[2],
hidden_layers_sizes=[10, 10],
reservoir_buffer_capacity=50,
batch_size=4,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_size=50,
q_replay_memory_init_size=memory_init_size,
q_batch_size=4,
q_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):
agent.sample_episode_policy()
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)
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)
def test_train(self):
norm_step = 100
memory_init_size = 20
step_num = 1000
sess = tf.InteractiveSession()
tf.Variable(0, name='global_step', trainable=False)
agent = NFSPAgent(sess=sess,
scope='nfsp',
action_num=2,
state_shape=[2],
hidden_layers_sizes=[10, 10],
reservoir_buffer_capacity=50,
batch_size=4,
min_buffer_size_to_learn=memory_init_size,
q_replay_memory_size=50,
q_replay_memory_init_size=memory_init_size,
q_batch_size=4,
q_norm_step=norm_step,
q_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):
agent.sample_episode_policy()
predicted_action = agent.step({
'obs': np.random.random_sample((2, )),
'legal_actions': [0, 1]
})
self.assertGreaterEqual(predicted_action, 0)
self.assertLessEqual(predicted_action, 1)
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_rl()
agent.train_sl()
sess.close()
tf.reset_default_graph()