def batch_evaluate(agent, env_name, seed, episodes, return_obss_actions=False, pixel=False):
num_envs = min(256, episodes)
envs = []
for i in range(num_envs):
env = gym.make(env_name)
if pixel:
env = RGBImgPartialObsWrapper(env)
envs.append(env)
env = ManyEnvs(envs)
logs = {
"num_frames_per_episode": [],
"return_per_episode": [],
"observations_per_episode": [],
"actions_per_episode": [],
"seed_per_episode": []
}
for i in tqdm(range((episodes + num_envs - 1) // num_envs)):
seeds = range(seed + i * num_envs, seed + (i + 1) * num_envs)
env.seed(seeds)
# Reset agent.
if hasattr(agent, 'reset'):
agent.reset()
many_obs = env.reset()
cur_num_frames = 0
num_frames = np.zeros((num_envs,), dtype='int64')
returns = np.zeros((num_envs,))
already_done = np.zeros((num_envs,), dtype='bool')
if return_obss_actions:
obss = [[] for _ in range(num_envs)]
actions = [[] for _ in range(num_envs)]
while (num_frames == 0).any():
action = agent.act_batch(many_obs)['action']
if return_obss_actions:
for i in range(num_envs):
if not already_done[i]:
obss[i].append(many_obs[i])
actions[i].append(action[i].item())
many_obs, reward, done, _ = env.step(action)
agent.analyze_feedback(reward, done)
done = np.array(done)
just_done = done & (~already_done)
returns += reward * just_done
cur_num_frames += 1
num_frames[just_done] = cur_num_frames
already_done[done] = True
logs["num_frames_per_episode"].extend(list(num_frames))
logs["return_per_episode"].extend(list(returns))
logs["seed_per_episode"].extend(list(seeds))
if return_obss_actions:
logs["observations_per_episode"].extend(obss)
logs["actions_per_episode"].extend(actions)
return logs
assert args.model is not None or args.demos is not None, "--model or --demos must be specified."
# if args.seed is None:
# args.seed = 0 if args.model is not None else 1
# Set seed for all randomness sources
utils.seed(args.seed)
# Generate environment
env = gym.make(args.env)
if args.model is not None and 'pixel' in args.model:
env = RGBImgPartialObsWrapper(env)
env.seed(args.seed)
global obs
obs = env.reset()
print("Mission: {}".format(obs["mission"]))
# Define agent
agent = utils.load_agent(env, args.model, args.demos, args.demos_origin,
args.argmax, args.env)
# Run the agent
done = True
action = None
def keyDownCb(event):
global obs
def generate_demos(n_episodes, valid, seed, shift=0):
utils.seed(seed)
# Generate environment
env = gym.make(args.env)
use_pixels = args.pixels
if use_pixels:
env = RGBImgPartialObsWrapper(env)
agent = utils.load_agent(env, args.model, args.demos, 'agent', args.argmax,
args.env)
demos_path = utils.get_demos_path(args.demos, args.env, 'agent', valid)
demos = []
checkpoint_time = time.time()
just_crashed = False
while True:
if len(demos) == n_episodes:
break
done = False
if just_crashed:
logger.info(
"reset the environment to find a mission that the bot can solve"
)
env.reset()
else:
env.seed(seed + len(demos))
obs = env.reset()
agent.on_reset()
actions = []
mission = obs["mission"]
images = []
directions = []
try:
while not done:
action = agent.act(obs)['action']
if isinstance(action, torch.Tensor):
action = action.item()
new_obs, reward, done, _ = env.step(action)
agent.analyze_feedback(reward, done)
actions.append(action)
images.append(obs['image'])
if use_pixels:
directions.append(None)
else:
directions.append(obs['direction'])
obs = new_obs
if reward > 0 and (args.filter_steps == 0
or len(images) <= args.filter_steps):
demos.append((mission, blosc.pack_array(np.array(images)),
directions, actions))
just_crashed = False
if reward == 0:
if args.on_exception == 'crash':
raise Exception(
"mission failed, the seed is {}".format(seed +
len(demos)))
just_crashed = True
logger.info("mission failed")
except (Exception, AssertionError):
if args.on_exception == 'crash':
raise
just_crashed = True
logger.exception("error while generating demo #{}".format(
len(demos)))
continue
if len(demos) and len(demos) % args.log_interval == 0:
now = time.time()
demos_per_second = args.log_interval / (now - checkpoint_time)
to_go = (n_episodes - len(demos)) / demos_per_second
logger.info(
"demo #{}, {:.3f} demos per second, {:.3f} seconds to go".
format(len(demos) - 1, demos_per_second, to_go))
checkpoint_time = now
# Save demonstrations
if args.save_interval > 0 and len(
demos) < n_episodes and len(demos) % args.save_interval == 0:
logger.info("Saving demos...")
utils.save_demos(demos, demos_path)
logger.info("{} demos saved".format(len(demos)))
# print statistics for the last 100 demonstrations
print_demo_lengths(demos[-100:])
# Save demonstrations
logger.info("Saving demos...")
utils.save_demos(demos, demos_path)
logger.info("{} demos saved".format(len(demos)))
print_demo_lengths(demos[-100:])
def batch_evaluate(agent,
env_name,
seed,
episodes,
return_obss_actions=False,
pixel=False,
monitor_gym=False,
pairs_dict=None,
model_path=None):
num_envs = min(256, episodes)
envs = []
for i in range(num_envs):
if '_c' in env_name:
env = gym.make(env_name, pairs_dict=pairs_dict, test_mode=True)
else:
env = gym.make(env_name)
if pixel:
env = RGBImgPartialObsWrapper(env)
if monitor_gym:
demo_path = os.path.join(model_path, 'gym_demos')
if not i % 64:
env = Monitor(
env,
demo_path,
video_callable=lambda episode_id: episode_id == 1,
force=True)
else:
env = Monitor(env, demo_path, video_callable=False, force=True)
envs.append(env)
env = ManyEnvs(envs)
logs = {
"num_frames_per_episode": [],
"return_per_episode": [],
"observations_per_episode": [],
"actions_per_episode": [],
"seed_per_episode": [],
"seen_missions": [env.mission for env in envs]
}
for i in range((episodes + num_envs - 1) // num_envs):
seeds = range(seed + i * num_envs, seed + (i + 1) * num_envs)
env.seed(seeds)
many_obs = env.reset()
cur_num_frames = 0
num_frames = np.zeros((num_envs, ), dtype='int64')
returns = np.zeros((num_envs, ))
already_done = np.zeros((num_envs, ), dtype='bool')
if return_obss_actions:
obss = [[] for _ in range(num_envs)]
actions = [[] for _ in range(num_envs)]
while (num_frames == 0).any():
action = agent.act_batch(many_obs)['action']
if return_obss_actions:
for i in range(num_envs):
if not already_done[i]:
obss[i].append(many_obs[i])
actions[i].append(action[i].item())
many_obs, reward, done, _ = env.step(action)
agent.analyze_feedback(reward, done)
done = np.array(done)
just_done = done & (~already_done)
returns += reward * just_done
cur_num_frames += 1
num_frames[just_done] = cur_num_frames
already_done[done] = True
logs["num_frames_per_episode"].extend(list(num_frames))
logs["return_per_episode"].extend(list(returns))
logs["seed_per_episode"].extend(list(seeds))
if return_obss_actions:
logs["observations_per_episode"].extend(obss)
logs["actions_per_episode"].extend(actions)
return logs