def test_step_back(self):
env = Env(allow_step_back=True)
state, player_id = env.init_game()
action = np.random.choice(state['legal_actions'])
env.step(action)
env.step_back()
self.assertEqual(env.game.round.current_player, player_id)
env = Env(allow_step_back=False)
state, player_id = env.init_game()
action = np.random.choice(state['legal_actions'])
env.step(action)
# env.step_back()
self.assertRaises(Exception, env.step_back)
def test_human_mode(self):
env = Env()
env.set_mode(human_mode=True)
state = env.reset()
self.assertIsInstance(state, dict)
for _ in range(100):
state, _, _ = env.step(np.random.choice(state['legal_actions']))
def test_decode_action(self):
env = Env()
env.init_game()
legal_actions = env.get_legal_actions()
for legal_action in legal_actions:
decoded = env.decode_action(legal_action)
self.assertLessEqual(decoded, ACTION_LIST[legal_action])
def test_get_legal_actions(self):
env = Env()
env.set_agents([RandomAgent(61), RandomAgent(61)])
env.init_game()
legal_actions = env.get_legal_actions()
for legal_action in legal_actions:
self.assertLessEqual(legal_action, 60)
def test_run(self):
env = Env()
env.set_agents([RandomAgent(309), RandomAgent(309)])
trajectories, payoffs = env.run(is_training=False)
self.assertEqual(len(trajectories), 2)
total = 0
for payoff in payoffs:
total += payoff
self.assertEqual(total, 0)
trajectories, payoffs = env.run(is_training=True, seed=1)
total = 0
for payoff in payoffs:
total += payoff
self.assertEqual(total, 0)
def test_step(self):
env = Env()
state, _ = env.init_game()
action = np.random.choice(state['legal_actions'])
_, player_id = env.step(action)
self.assertEqual(player_id, env.game.round.current_player)
def test_init_game_and_extract_state(self):
env = Env()
state, _ = env.init_game()
self.assertEqual(state['obs'].size, 420)