def make_gym_env(name,
seed=None,
frame_op=None,
frame_op_len=None,
reward_scale=None,
normalize_state=False):
'''General method to create any Gym env; auto wraps Atari'''
env = gym.make(name)
if seed is not None:
env.seed(seed)
if 'NoFrameskip' in env.spec.id: # Atari
env = wrap_atari(env)
# no reward clipping to allow monitoring; Atari memory clips it
episode_life = not util.in_eval_lab_modes()
env = wrap_deepmind(env, episode_life, frame_op_len)
elif len(env.observation_space.shape) == 3: # image-state env
env = PreprocessImage(env)
if normalize_state:
env = NormalizeStateEnv(env)
if frame_op_len is not None: # use concat for image (1, 84, 84)
env = FrameStack(env, 'concat', frame_op_len)
else: # vector-state env
if normalize_state:
env = NormalizeStateEnv(env)
if frame_op is not None:
env = FrameStack(env, frame_op, frame_op_len)
if reward_scale is not None:
env = ScaleRewardEnv(env, reward_scale)
return env
def train(self):
'''Train actor critic by computing the loss in batch efficiently'''
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
batch = self.sample()
clock.set_batch_size(len(batch))
pdparams, v_preds = self.calc_pdparam_v(batch)
advs, v_targets = self.calc_advs_v_targets(batch, v_preds)
policy_loss = self.calc_policy_loss(batch, pdparams, advs) # from actor
val_loss = self.calc_val_loss(v_preds, v_targets) # from critic
if self.shared: # shared network
loss = policy_loss + val_loss
self.net.train_step(loss, self.optim, self.lr_scheduler, clock=clock, global_net=self.global_net)
else:
self.net.train_step(policy_loss, self.optim, self.lr_scheduler, clock=clock, global_net=self.global_net)
self.critic_net.train_step(val_loss, self.critic_optim, self.critic_lr_scheduler, clock=clock, global_net=self.global_critic_net)
loss = policy_loss + val_loss
# reset
self.to_train = 0
logger.debug(f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.total_reward}, loss: {loss:g}')
return loss.item()
else:
return np.nan
def __init__(self, spec):
super().__init__(spec)
try_register_env(spec) # register if it's a custom gym env
seed = ps.get(spec, 'meta.random_seed')
episode_life = not util.in_eval_lab_modes()
if self.is_venv: # make vector environment
self.u_env = make_gym_venv(name=self.name,
num_envs=self.num_envs,
seed=seed,
frame_op=self.frame_op,
frame_op_len=self.frame_op_len,
image_downsize=self.image_downsize,
reward_scale=self.reward_scale,
normalize_state=self.normalize_state,
episode_life=episode_life)
else:
self.u_env = make_gym_env(name=self.name,
seed=seed,
frame_op=self.frame_op,
frame_op_len=self.frame_op_len,
image_downsize=self.image_downsize,
reward_scale=self.reward_scale,
normalize_state=self.normalize_state,
episode_life=episode_life)
if self.name.startswith('Unity'):
# Unity is always initialized as singleton gym env, but the Unity runtime can be vec_env
self.num_envs = self.u_env.num_envs
# update variables dependent on num_envs
self._infer_venv_attr()
self._set_clock()
self._set_attr_from_u_env(self.u_env)
self.max_t = self.max_t or self.u_env.spec.max_episode_steps
assert self.max_t is not None
logger.info(util.self_desc(self))
def train(self):
'''
Completes one training step for the agent if it is time to train.
i.e. the environment timestep is greater than the minimum training timestep and a multiple of the training_frequency.
Each training step consists of sampling n batches from the agent's memory.
For each of the batches, the target Q values (q_targets) are computed and a single training step is taken k times
Otherwise this function does nothing.
'''
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
total_loss = torch.tensor(0.0)
for _ in range(self.training_iter):
batch = self.sample()
clock.set_batch_size(len(batch))
for _ in range(self.training_batch_iter):
loss = self.calc_q_loss(batch)
self.net.train_step(loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
total_loss += loss
loss = total_loss / (self.training_iter * self.training_batch_iter)
# reset
self.to_train = 0
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.env.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan
def update(self, algorithm, clock):
'''Get an updated value for var'''
if (util.in_eval_lab_modes()) or self._updater_name == 'no_decay':
return self.end_val
step = clock.get()
val = self._updater(self.start_val, self.end_val, self.start_step, self.end_step, step)
return val
def train(self):
'''
Completes one training step for the agent if it is time to train.
i.e. the environment timestep is greater than the minimum training timestep and a multiple of the training_frequency.
Each training step consists of sampling n batches from the agent's memory.
For each of the batches, the target Q values (q_targets) are computed and a single training step is taken k times
Otherwise this function does nothing.
'''
if util.in_eval_lab_modes():
self.body.flush()
return np.nan
clock = self.body.env.clock
tick = clock.get(clock.max_tick_unit)
self.to_train = (tick > self.training_start_step and tick % self.training_frequency == 0)
if self.to_train == 1:
total_loss = torch.tensor(0.0, device=self.net.device)
for _ in range(self.training_epoch):
batch = self.sample()
for _ in range(self.training_batch_epoch):
loss = self.calc_q_loss(batch)
self.net.training_step(loss=loss, lr_clock=clock)
total_loss += loss
loss = total_loss / (self.training_epoch * self.training_batch_epoch)
# reset
self.to_train = 0
self.body.flush()
logger.debug(f'Trained {self.name} at epi: {clock.epi}, total_t: {clock.total_t}, t: {clock.t}, total_reward so far: {self.body.memory.total_reward}, loss: {loss:g}')
return loss.item()
else:
return np.nan
def train(self):
'''
Completes one training step for the agent if it is time to train.
Otherwise this function does nothing.
'''
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
batch = self.sample()
clock.set_batch_size(len(batch))
loss = self.calc_q_loss(batch)
self.net.train_step(loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
# reset
self.to_train = 0
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan
def random(state, algorithm, body):
'''Random action using gym.action_space.sample(), with the same format as default()'''
if body.env.is_venv and not util.in_eval_lab_modes():
_action = [body.action_space.sample() for _ in range(body.env.num_envs)]
else:
_action = [body.action_space.sample()]
action = torch.tensor(_action)
return action
def act(self, state):
'''Random action'''
body = self.body
if body.env.is_venv and not util.in_eval_lab_modes():
action = np.array(
[body.action_space.sample() for _ in range(body.env.num_envs)])
else:
action = body.action_space.sample()
return action
def train(self):
'''Trains the algorithm'''
if util.in_eval_lab_modes():
self.body.flush()
return np.nan
if self.shared:
return self.train_shared()
else:
return self.train_separate()
def guard_tensor(state, body):
'''Guard-cast tensor before being input to network'''
if isinstance(state, LazyFrames):
state = state.__array__() # realize data
state = torch.from_numpy(state.astype(np.float32))
if not body.env.is_venv or util.in_eval_lab_modes():
# singleton state, unsqueeze as minibatch for net input
state = state.unsqueeze(dim=0)
return state
def try_scale_reward(cls, reward):
'''Env class to scale reward'''
if util.in_eval_lab_modes(): # only trigger on training
return reward
if cls.reward_scale is not None:
if cls.sign_reward:
reward = np.sign(reward)
else:
reward *= cls.reward_scale
return reward
def space_train(self):
if util.in_eval_lab_modes():
return np.nan
losses = []
for body in self.agent.nanflat_body_a:
self.body = body
losses.append(self.train())
# set body reference back to default
self.body = self.agent.nanflat_body_a[0]
loss_a = self.nanflat_to_data_a('loss', losses)
return loss_a
def update(self, state, action, reward, next_state, done):
'''Update per timestep after env transitions, e.g. memory, algorithm, update agent params, train net'''
self.body.update(state, action, reward, next_state, done)
if util.in_eval_lab_modes(): # eval does not update agent for training
return
self.body.memory.update(state, action, reward, next_state, done)
loss = self.algorithm.train()
if not np.isnan(loss): # set for log_summary()
self.body.loss = loss
explore_var = self.algorithm.update()
return loss, explore_var
def to_ckpt(self, env, mode='eval'):
'''Check with clock whether to run log/eval ckpt: at the start, save_freq, and the end'''
if mode == 'eval' and util.in_eval_lab_modes(
): # avoid double-eval: eval-ckpt in eval mode
return False
clock = env.clock
frame = clock.get()
frequency = env.eval_frequency if mode == 'eval' else env.log_frequency
to_ckpt = util.frame_mod(frame, frequency,
env.num_envs) or frame == clock.max_frame
return to_ckpt
def post_init_nets(self):
'''
Method to conditionally load models.
Call at the end of init_nets() after setting self.net_names
'''
assert hasattr(self, 'net_names')
if util.in_eval_lab_modes():
logger.info(
f'Loaded algorithm models for lab_mode: {util.get_lab_mode()}')
self.load()
else:
logger.info(
f'Initialized algorithm models for lab_mode: {util.get_lab_mode()}'
)
def train(self):
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
net_util.copy(self.net, self.old_net) # update old net
batch = self.sample()
clock.set_batch_size(len(batch))
with torch.no_grad():
states = batch['states']
if self.body.env.is_venv:
states = math_util.venv_unpack(states)
# NOTE states is massive with batch_size = time_horizon * num_envs. Chunk up so forward pass can fit into device esp. GPU
num_chunks = int(len(states) / self.minibatch_size)
v_preds_chunks = [self.calc_v(states_chunk, use_cache=False) for states_chunk in torch.chunk(states, num_chunks)]
v_preds = torch.cat(v_preds_chunks)
advs, v_targets = self.calc_advs_v_targets(batch, v_preds)
# piggy back on batch, but remember to not pack or unpack
batch['advs'], batch['v_targets'] = advs, v_targets
if self.body.env.is_venv: # unpack if venv for minibatch sampling
for k, v in batch.items():
if k not in ('advs', 'v_targets'):
batch[k] = math_util.venv_unpack(v)
total_loss = torch.tensor(0.0)
for _ in range(self.training_epoch):
minibatches = util.split_minibatch(batch, self.minibatch_size)
for minibatch in minibatches:
if self.body.env.is_venv: # re-pack to restore proper shape
for k, v in minibatch.items():
if k not in ('advs', 'v_targets'):
minibatch[k] = math_util.venv_pack(v, self.body.env.num_envs)
advs, v_targets = minibatch['advs'], minibatch['v_targets']
pdparams, v_preds = self.calc_pdparam_v(minibatch)
policy_loss = self.calc_policy_loss(minibatch, pdparams, advs) # from actor
val_loss = self.calc_val_loss(v_preds, v_targets) # from critic
if self.shared: # shared network
loss = policy_loss + val_loss
self.net.train_step(loss, self.optim, self.lr_scheduler, clock=clock, global_net=self.global_net)
else:
self.net.train_step(policy_loss, self.optim, self.lr_scheduler, clock=clock, global_net=self.global_net)
self.critic_net.train_step(val_loss, self.critic_optim, self.critic_lr_scheduler, clock=clock, global_net=self.global_critic_net)
loss = policy_loss + val_loss
total_loss += loss
loss = total_loss / self.training_epoch / len(minibatches)
# reset
self.to_train = 0
logger.debug(f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.env.total_reward}, loss: {loss:g}')
return loss.item()
else:
return np.nan
def to_ckpt(self, env, mode='eval'):
'''Check with clock whether to run log/eval ckpt: at the start, save_freq, and the end'''
if mode == 'eval' and util.in_eval_lab_modes(
): # avoid double-eval: eval-ckpt in eval mode
return False
clock = env.clock
frame = clock.get()
frequency = env.eval_frequency if mode == 'eval' else env.log_frequency
if frequency is None: # default episodic
to_ckpt = env.done
else: # normal ckpt condition by mod remainder (general for venv)
to_ckpt = util.frame_mod(frame, frequency,
env.num_envs) or frame == clock.max_frame
return to_ckpt
def gather_aeb_rewards_df(aeb, session_datas, max_tick_unit):
'''Gather rewards from each session for a body into a df'''
aeb_session_rewards = {}
for s, session_data in session_datas.items():
aeb_df = session_data[aeb]
aeb_reward_sr = aeb_df['reward_ma']
aeb_reward_sr.index = aeb_df[max_tick_unit]
# guard for duplicate eval result
aeb_reward_sr = aeb_reward_sr[~aeb_reward_sr.index.duplicated()]
if util.in_eval_lab_modes():
# guard for eval appending possibly not ordered
aeb_reward_sr.sort_index(inplace=True)
aeb_session_rewards[s] = aeb_reward_sr
aeb_rewards_df = pd.DataFrame(aeb_session_rewards)
return aeb_rewards_df
def load_algorithm(algorithm):
'''Save all the nets for an algorithm'''
agent = algorithm.agent
net_names = algorithm.net_names
if util.in_eval_lab_modes():
# load specific model in eval mode
prepath = agent.info_space.eval_model_prepath
else:
prepath = util.get_prepath(agent.spec, agent.info_space, unit='session')
logger.info(f'Loading algorithm {util.get_class_name(algorithm)} nets {net_names}')
for net_name in net_names:
net = getattr(algorithm, net_name)
model_path = f'{prepath}_{net_name}_model.pth'
load(net, model_path)
optim_path = f'{prepath}_{net_name}_optim.pth'
load(net.optim, optim_path)
def __init__(self, spec):
self.done = False
self.env_spec = spec['env'][0] # idx 0 for single-env
# set default
util.set_attr(
self,
dict(
log_frequency=None, # default to log at epi done
frame_op=None,
frame_op_len=None,
normalize_state=False,
reward_scale=None,
num_envs=None,
))
util.set_attr(self, spec['meta'], [
'log_frequency',
'eval_frequency',
])
util.set_attr(self, self.env_spec, [
'name',
'frame_op',
'frame_op_len',
'normalize_state',
'reward_scale',
'num_envs',
'max_t',
'max_frame',
])
seq_len = ps.get(spec, 'agent.0.net.seq_len')
if seq_len is not None: # infer if using RNN
self.frame_op = 'stack'
self.frame_op_len = seq_len
if util.in_eval_lab_modes(): # use singleton for eval
self.num_envs = 1
self.log_frequency = None
if spec['meta'][
'distributed'] != False: # divide max_frame for distributed
self.max_frame = int(self.max_frame / spec['meta']['max_session'])
self.is_venv = (self.num_envs is not None and self.num_envs > 1)
if self.is_venv:
assert self.log_frequency is not None, f'Specify log_frequency when using venv'
self.clock_speed = 1 * (
self.num_envs or 1
) # tick with a multiple of num_envs to properly count frames
self.clock = Clock(self.max_frame, self.clock_speed)
self.to_render = util.to_render()
def try_ckpt(self, agent, env):
'''Try to checkpoint agent at the start, save_freq, and the end'''
tick = env.clock.get(env.max_tick_unit)
to_ckpt = False
if not util.in_eval_lab_modes() and tick <= env.max_tick:
to_ckpt = (tick % env.eval_frequency == 0) or tick == env.max_tick
if env.max_tick_unit == 'epi': # extra condition for epi
to_ckpt = to_ckpt and env.done
if to_ckpt:
if self.spec['meta'].get('parallel_eval'):
retro_analysis.run_parallel_eval(self, agent, env)
else:
self.run_eval_episode()
if analysis.new_best(agent):
agent.save(ckpt='best')
if tick > 0: # nothing to analyze at start
analysis.analyze_session(self, eager_analyze_trial=True)
def __init__(self, spec):
super().__init__(spec)
try_register_env(spec) # register if it's a custom gym env
seed = ps.get(spec, 'meta.random_seed')
episode_life = not util.in_eval_lab_modes()
if self.is_venv: # make vector environment
self.u_env = make_gym_venv(self.name, self.num_envs, seed,
self.frame_op, self.frame_op_len,
self.reward_scale, self.normalize_state,
episode_life)
else:
self.u_env = make_gym_env(self.name, seed, self.frame_op,
self.frame_op_len, self.reward_scale,
self.normalize_state, episode_life)
self._set_attr_from_u_env(self.u_env)
self.max_t = self.max_t or self.u_env.spec.max_episode_steps
assert self.max_t is not None
logger.info(util.self_desc(self))
def train(self):
if util.in_eval_lab_modes():
self.body.flush()
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
batch = self.sample()
loss = self.calc_policy_loss(batch)
self.net.training_step(loss=loss, lr_clock=clock)
# reset
self.to_train = 0
self.body.flush()
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, total_t: {clock.total_t}, t: {clock.t}, total_reward so far: {self.body.memory.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan
def train(self):
'''Train actor critic by computing the loss in batch efficiently'''
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
for _ in range(self.training_iter):
batch = self.sample()
clock.set_batch_size(len(batch))
states = batch['states']
actions = self.guard_q_actions(batch['actions'])
q_targets = self.calc_q_targets(batch)
# Q-value loss for both Q nets
q1_preds = self.calc_q(states, actions, self.q1_net)
q1_loss = self.calc_reg_loss(q1_preds, q_targets)
self.q1_net.train_step(q1_loss, self.q1_optim, self.q1_lr_scheduler, clock=clock, global_net=self.global_q1_net)
q2_preds = self.calc_q(states, actions, self.q2_net)
q2_loss = self.calc_reg_loss(q2_preds, q_targets)
self.q2_net.train_step(q2_loss, self.q2_optim, self.q2_lr_scheduler, clock=clock, global_net=self.global_q2_net)
# policy loss
action_pd = policy_util.init_action_pd(self.body.ActionPD, self.calc_pdparam(states))
log_probs, reparam_actions = self.calc_log_prob_action(action_pd, reparam=True)
policy_loss = self.calc_policy_loss(batch, log_probs, reparam_actions)
self.net.train_step(policy_loss, self.optim, self.lr_scheduler, clock=clock, global_net=self.global_net)
# alpha loss
alpha_loss = self.calc_alpha_loss(log_probs)
self.train_alpha(alpha_loss)
loss = q1_loss + q2_loss + policy_loss + alpha_loss
# update target networks
self.update_nets()
# update PER priorities if availalbe
self.try_update_per(torch.min(q1_preds, q2_preds), q_targets)
# reset
self.to_train = 0
logger.debug(f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.env.total_reward}, loss: {loss:g}')
return loss.item()
else:
return np.nan
def load_algorithm(algorithm):
'''Save all the nets for an algorithm'''
agent = algorithm.agent
net_names = algorithm.net_names
if util.in_eval_lab_modes():
# load specific model in eval mode
model_prepath = agent.spec['meta']['eval_model_prepath']
else:
model_prepath = agent.spec['meta']['model_prepath']
logger.info(
f'Loading algorithm {util.get_class_name(algorithm)} nets {net_names} from {model_prepath}_*.pt'
)
for net_name in net_names:
net = getattr(algorithm, net_name)
model_path = f'{model_prepath}_{net_name}_model.pt'
load(net, model_path)
optim_name = net_name.replace('net', 'optim')
optim = getattr(algorithm, optim_name, None)
if optim is not None: # only trainable net has optim
optim_path = f'{model_prepath}_{net_name}_optim.pt'
load(optim, optim_path)
def save_session_df(session_data, filepath, info_space):
'''Save session_df, and if is in eval mode, modify it and save with append'''
if util.in_eval_lab_modes():
ckpt = util.find_ckpt(info_space.eval_model_prepath)
epi = int(re.search('epi(\d+)', ckpt)[1])
totalt = int(re.search('totalt(\d+)', ckpt)[1])
session_df = pd.concat(session_data, axis=1)
mean_sr = session_df.mean()
mean_sr.name = totalt # set index to prevent all being the same
eval_session_df = pd.DataFrame(data=[mean_sr])
# set sr name too, to total_t
for aeb in util.get_df_aeb_list(eval_session_df):
eval_session_df.loc[:, aeb + ('epi',)] = epi
eval_session_df.loc[:, aeb + ('total_t',)] = totalt
# if eval, save with append mode
header = not os.path.exists(filepath)
with open(filepath, 'a') as f:
eval_session_df.to_csv(f, header=header)
else:
session_df = pd.concat(session_data, axis=1)
util.write(session_df, filepath)
def train(self):
'''
Completes one training step for the agent if it is time to train.
Otherwise this function does nothing.
'''
if util.in_eval_lab_modes():
self.body.flush()
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
batch = self.sample()
loss = self.calc_q_loss(batch)
self.net.training_step(loss=loss, lr_clock=clock)
# reset
self.to_train = 0
self.body.flush()
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, total_t: {clock.total_t}, t: {clock.t}, total_reward so far: {self.body.memory.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan
def __init__(self, spec):
self.env_spec = spec['env'][0] # idx 0 for single-env
# set default
util.set_attr(
self,
dict(
eval_frequency=10000,
log_frequency=10000,
frame_op=None,
frame_op_len=None,
image_downsize=(84, 84),
normalize_state=False,
reward_scale=None,
num_envs=1,
))
util.set_attr(self, spec['meta'], [
'eval_frequency',
'log_frequency',
])
util.set_attr(self, self.env_spec, [
'name',
'frame_op',
'frame_op_len',
'image_downsize',
'normalize_state',
'reward_scale',
'num_envs',
'max_t',
'max_frame',
])
# override if env is for eval
if util.in_eval_lab_modes():
self.num_envs = ps.get(spec, 'meta.rigorous_eval')
self.to_render = util.to_render()
self._infer_frame_attr(spec)
self._infer_venv_attr()
self._set_clock()
self.done = False
self.total_reward = np.nan
def train(self):
if util.in_eval_lab_modes():
return np.nan
clock = self.body.env.clock
if self.to_train == 1:
batch = self.sample()
clock.set_batch_size(len(batch))
pdparams = self.calc_pdparam_batch(batch)
advs = self.calc_ret_advs(batch)
loss = self.calc_policy_loss(batch, pdparams, advs)
self.net.train_step(loss,
self.optim,
self.lr_scheduler,
clock=clock,
global_net=self.global_net)
# reset
self.to_train = 0
logger.debug(
f'Trained {self.name} at epi: {clock.epi}, frame: {clock.frame}, t: {clock.t}, total_reward so far: {self.body.env.total_reward}, loss: {loss:g}'
)
return loss.item()
else:
return np.nan
