def test_move_to_beacon(self):
with sc2_env.SC2Env(
"MoveToBeacon",
step_mul=self.step_mul,
game_steps_per_episode=self.steps * self.step_mul) as env:
agent = scripted_agent.MoveToBeacon()
run_loop.run_loop([agent], env, self.steps)
# Get some points
self.assertLessEqual(agent.episodes, agent.reward)
self.assertEqual(agent.steps, self.steps)
def test_move_to_beacon(self):
with sc2_env.SC2Env(
map_name="MoveToBeacon",
agent_interface_format=sc2_env.AgentInterfaceFormat(
feature_dimensions=sc2_env.Dimensions(
screen=84,
minimap=64)),
step_mul=self.step_mul,
game_steps_per_episode=self.steps * self.step_mul) as env:
agent = scripted_agent.MoveToBeacon()
run_loop.run_loop([agent], env, self.steps)
# Get some points
self.assertLessEqual(agent.episodes, agent.reward)
self.assertEqual(agent.steps, self.steps)
def evaluate_scripted_agent(n_episodes=100, max_steps=120):
game_params = dict(
feature_screen=16, # screen resolution in pixel
feature_minimap=
16, # minimap resolution in pixel (smaller or equal to screen)
action_space="FEATURES"
) # either FEATURES or RGB - suggested: FEATURES
performance = []
env = init_game(game_params, max_steps)
agent = scripted_agent.MoveToBeacon()
for e in range(n_episodes):
if (e + 1) % 10 == 0:
print("Episode %d / %d" % (e + 1, n_episodes))
rewards = play_episode(agent, env, max_steps)
performance.append(np.sum(rewards))
performance = np.array(performance)
return performance.mean(), performance.std(), performance