#KOETODO: I think I can remove these. storing straight to file instead.
mavg_score = np.mean(np.array(reward_buffer))
var_score = np.var(np.array(reward_buffer))
mavg_food = np.mean(np.array(food_buffer))
mavg_poison = np.mean(np.array(poison_buffer))
mavg_loss = np.mean(loss_buffer)
food_buffer = []
poison_buffer = []
reward_buffer = []
loss_buffer = []
# Write Rolling Statistics to file
with open(SAVE_TO_FOLDER + "/dfp_stats.txt",
"a+") as stats_file:
stats_file.write(str(GAME) + " ")
stats_file.write(str(max_reward) + " ")
stats_file.write(str(mavg_score) + ' ')
stats_file.write(str(mavg_loss) + ' ')
stats_file.write(str(var_score) + ' ')
stats_file.write(str(mavg_food) + ' ')
stats_file.write(str(mavg_poison) + '\n')
env.close()
end = time.time()
time_elapsed = end - start
with open(SAVE_TO_FOLDER + "/timing_info.txt", "w") as text_file:
print("Time Elapsed: {}".format(time_elapsed), file=text_file)
#KOE: Made it to the end. Now test running, print out, debug, etc.
class UnityEnvWrapper:
def __init__(self, env_config=None, use_eval=False, rpc_mode=False):
self.env = None
if not rpc_mode:
assert (env_config is not None)
self.launch(env_config, use_eval)
def launch(self, env_config, use_eval=False):
environment_path = (env_config["environment_path_eval"]
if use_eval else env_config["environment_path"])
port = env_config.get("port", 0)
if use_eval and port:
port += 2
use_visual = env_config.get("use_visual", False)
use_vector = env_config.get("use_vector", True)
multiagent = env_config.get("multiagent", False)
uint8_visual = env_config.get("uint8_visual", True)
flatten_branched = env_config.get("flatten_branched", True)
self.env = UnityEnv(
environment_path,
port,
use_visual=use_visual,
use_vector=use_vector,
uint8_visual=uint8_visual,
multiagent=multiagent,
flatten_branched=flatten_branched,
)
self.action_space = self.env._action_space
self.observation_space = self.env._observation_space
# agent name must be unique among **all** agents
self.agent_name = [
f'{port}_{i}' for i in range(self.env.number_agents)
]
def _transform_list_to_dict(self, objs):
return {name: obj for name, obj in zip(self.agent_name, objs)}
def _transform_dict_to_list(self, objs):
return [objs[name] for name in self.agent_name]
def step(self, act, action_settings=None):
action = np.stack(self._transform_dict_to_list(act)).tolist()
observation, reward, done, info = self.env.step(action)
transform = self._transform_list_to_dict
info = list(map(json.loads, info['text_observation']))
for i, x in enumerate(info):
x['done'] = done[i]
done = [False] * 4
done_dict = transform(done)
done_dict['__all__'] = False # no early termination (for logging)
return transform(observation), transform(reward), done_dict, transform(
info)
def reset(self, reset_settings=None):
obs = self.env.reset()
return self._transform_list_to_dict(obs)
def get_env_spaces(self):
spaces = self.action_space, self.observation_space, self.agent_name
p = pickle.dumps(spaces)
z = zlib.compress(p)
return z
def get_action_count(self):
if isinstance(self.env.action_space, gym.spaces.Discrete):
return self.env.action_space.n
elif isinstance(self.env.action_space, gym.spaces.MultiDiscrete):
return self.env.action_space.nvec.tolist()
raise NotImplementedError
def sample(self):
return self.env.action_space.sample()
def number_agents(self):
return self.env.number_agents
def env_close(self):
if self.env:
self.env.close()
self.env = None
def close(self):
self.env_close()
def hello(self):
print('Hello World')
