envs = VecEnv([
make_visual_env('./scenarios/deathmatch_maze.cfg')
for i in range(num_envs)
])
else:
envs = VecEnv([
make_env(0, './scenarios/deathmatch_maze.cfg') for i in range(num_envs)
])
# Define some actions. Each list entry corresponds to declared buttons:
# MOVE_LEFT, MOVE_RIGHT, ATTACK
# 5 more combinations are naturally possible but only 3 are included for transparency when watching.
# actions = [[True, False, False], [False, True, False], [False, False, True]]
actions = range(envs.action_space_shape)
episode_num = 0
while True:
print('Episode #', episode_num)
for j in range(1000):
action_array = [choice(actions) for i in range(num_envs)]
# print (action_array)
obs, reward, done, info = envs.step(action_array)
if done:
game_vars = envs.get_game_variables(0)
print('Kills : ', game_vars[2])
episode_num += 1
break
# print ('Reward:', reward)
sleep(0.01)
envs.reset()
sleep(0.1)
while episode_cnt < num_episodes:
# sleep(0.01)
print(actor_critic.get_probs(Variable(current_obs, volatile=True)))
value, action = actor_critic.act(Variable(current_obs, volatile=True),
deterministic=True)
cpu_actions = action.data.cpu().numpy()
print('Action:', [cpu_actions[0]])
# Obser reward and next obs
obs, reward, done, _ = envs.step([cpu_actions[0]])
episode_reward += reward[0]
if done:
total_reward += episode_reward
episode_cnt += 1
episode_reward = 0.0
episode_game_variables = envs.get_game_variables(0)
if episode_game_variables != None:
total_kills += episode_game_variables[2]
obs = envs.reset()
actor_critic = torch.load(
os.path.join(args.load_dir, args.env_name + ".pt"))
actor_critic.eval()
update_current_obs(obs)
print('Avg reward:', round(total_reward / num_episodes))
print('Avg kills:', (total_kills / num_episodes))
envs.close()
