def test_step_back(self):
env = Env(allow_step_back=True)
_, player_id = env.init_game()
env.step(0)
_, back_player_id = env.step_back()
self.assertEqual(player_id, back_player_id)
self.assertEqual(env.step_back(), False)
env = Env()
with self.assertRaises(Exception):
env.step_back()
def test_step(self):
env = Env()
state, player_id = env.init_game()
self.assertEqual(player_id, env.get_player_id())
action = state['legal_actions'][0]
_, player_id = env.step(action)
self.assertEqual(player_id, env.get_player_id())
def test_set_mode(self):
env = Env()
with self.assertRaises(ValueError):
env.set_mode()
with self.assertRaises(ValueError):
env.set_mode(active_player=100)
with self.assertRaises(ValueError):
env.set_mode(single_agent_mode=True, human_mode=True)
def test_run(self):
env = Env()
agents = [RandomAgent(env.action_num) for _ in range(env.player_num)]
env.set_agents(agents)
trajectories, payoffs = env.run(is_training=False)
self.assertEqual(len(trajectories), 2)
total = 0
for payoff in payoffs:
total += payoff
self.assertEqual(total, 0)
def test_human_mode(self):
env = Env()
with self.assertRaises(ValueError):
env.reset()
env.set_mode(human_mode=True)
with self.assertRaises(ValueError):
env.set_agents([])
with self.assertRaises(ValueError):
env.run()
state = env.reset()
self.assertIsInstance(state, dict)
for _ in range(100):
state, _, _ = env.step(np.random.choice(state['legal_actions']))
def test_init_game_and_extract_state(self):
env = Env()
state, _ = env.init_game()
self.assertEqual(state['obs'].size, 6)
for action in state['legal_actions']:
self.assertLess(action, env.action_num)
def test_decode_action(self):
env = Env()
state, _ = env.init_game()
for action in state['legal_actions']:
decoded = env.decode_action(action)
self.assertIn(decoded, env.actions)
def test_get_legal_actions(self):
env = Env()
env.init_game()
legal_actions = env.get_legal_actions()
for action in legal_actions:
self.assertIn(action, env.actions)