411 lines
14 KiB
Python
411 lines
14 KiB
Python
# %%
|
|
import sys
|
|
sys.path.append('../../../')
|
|
from src.discriminator import CnnDiscriminator, CnnDiscriminatorFlatAction
|
|
from src.policies import OptionsCnnPolicy
|
|
from src.util import feasible
|
|
from src.data import load_experts
|
|
|
|
from imitation.algorithms import adversarial
|
|
from imitation.util import logger
|
|
import imitation.data.rollout as rollout
|
|
|
|
import stable_baselines3
|
|
from stable_baselines3.common.env_util import make_vec_env
|
|
|
|
import torch
|
|
import torch.utils.data
|
|
import numpy as np
|
|
import itertools
|
|
import gym
|
|
import pickle
|
|
import tempfile
|
|
import pathlib
|
|
from tqdm import tqdm
|
|
|
|
from intersim.envs.intersimple import NRasterized, NRasterizedRandomAgent, NRasterizedIncrementingAgent
|
|
|
|
ALL_OPTIONS = [(v,t) for v in [0,2,4,6,8] for t in [5, 10, 20]] # option 0 is safe fallback
|
|
|
|
class OptionsEnv(gym.Wrapper):
|
|
"""
|
|
Wrap an intersimple environment with an options generator
|
|
"""
|
|
def __init__(self, env, *args, **kwargs):
|
|
"""
|
|
Initialize wrapped environment and set high-level action and observation spaces
|
|
"""
|
|
super().__init__(env, *args, **kwargs)
|
|
num_hl_options = len(ALL_OPTIONS)
|
|
self.action_space = gym.spaces.Discrete(num_hl_options)
|
|
self.observation_space = gym.spaces.Dict({
|
|
'obs': env.observation_space,
|
|
'mask': gym.spaces.Box(low=0, high=1, shape=(num_hl_options,)),
|
|
})
|
|
|
|
def _after_choice(self):
|
|
pass
|
|
|
|
def _after_step(self):
|
|
pass
|
|
|
|
def _transitions(self):
|
|
raise NotImplementedError('Use `LLOptions` or `HLOptions` for sampling.')
|
|
|
|
def sample(self, generator):
|
|
"""
|
|
yield transitions using a generator
|
|
Args:
|
|
generator (sb3.PPO)
|
|
Yields:
|
|
|
|
"""
|
|
self.done = True
|
|
while True:
|
|
self.episode_start = False
|
|
if self.done:
|
|
self.s = self.env.reset()
|
|
self.done = False
|
|
self.episode_start = True
|
|
|
|
self.m = available_actions(self.env)
|
|
self.ch, self.value, self.log_prob = generator.policy.predict({
|
|
'obs': torch.tensor(self.s).unsqueeze(0).to(generator.policy.device),
|
|
'mask': torch.tensor(self.m).unsqueeze(0).to(generator.policy.device),
|
|
})
|
|
self.plan = list(map(float, generate_plan(self.env, self.ch)))
|
|
|
|
self._after_choice()
|
|
|
|
assert not self.done
|
|
assert self.plan
|
|
#assert feasible(self.env, self.plan, self.ch)
|
|
|
|
while not self.done and self.plan and feasible(self.env, self.plan, self.ch):
|
|
self.a, self.plan = self.plan[0], self.plan[1:]
|
|
self.a = self.env._normalize(self.a)
|
|
self.nexts, _, self.done, _ = self.env.step(self.a)
|
|
|
|
self._after_step()
|
|
|
|
self.s = self.nexts
|
|
|
|
yield from self._transitions()
|
|
|
|
class LLOptions(OptionsEnv):
|
|
"""Sample low-level (state, action) tuples for discriminator training."""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
"""
|
|
LLOption uses the true LL observations
|
|
"""
|
|
super().__init__(*args, **kwargs)
|
|
# overwrite observation space to just output obs directly
|
|
self.observation_space = self.observation_space['obs']
|
|
|
|
def _after_choice(self):
|
|
"""
|
|
After each option choice, initialize/reset the transition buffer
|
|
"""
|
|
self._transition_buffer = []
|
|
|
|
def _after_step(self):
|
|
"""
|
|
After each ll action, append s, s', a, done to transition buffer
|
|
"""
|
|
self._transition_buffer.append({
|
|
'obs': self.s,
|
|
'next_obs': self.nexts,
|
|
'acts': np.array((self.a,)),
|
|
'dones': np.array(self.done),
|
|
})
|
|
|
|
def _transitions(self):
|
|
"""
|
|
Yield from the transition buffer
|
|
"""
|
|
yield from self._transition_buffer
|
|
|
|
def sample_ll(self, policy):
|
|
"""
|
|
Args:
|
|
policy
|
|
Returns:
|
|
gen: iterable which samples low-level transitions from the environment
|
|
"""
|
|
return self.sample(policy)
|
|
|
|
class HLOptions(OptionsEnv):
|
|
"""Sample high-level (state, action, reward) tuples for generator training."""
|
|
|
|
def __init__(self, *args, **kwargs):
|
|
super().__init__(*args, **kwargs)
|
|
|
|
def _after_choice(self):
|
|
"""
|
|
After an option selection, initialize total reward and number of steps
|
|
"""
|
|
self.obs = {'obs': np.copy(self.s), 'mask': np.copy(self.m)}
|
|
self.r = 0
|
|
self.steps = 0
|
|
|
|
def _after_step(self):
|
|
"""
|
|
After each low-level action, add the discounted discriminated reward score (given a discriminator)
|
|
"""
|
|
self.r += self.discount**self.steps * self.discriminator.discrim_net.reward_train(
|
|
state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()),
|
|
action=torch.tensor([[self.a]]).to(self.discriminator.discrim_net.device()),
|
|
next_state=torch.tensor(self.s).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
|
|
done=torch.tensor(self.done).unsqueeze(0).to(self.discriminator.discrim_net.device()), # unused
|
|
)
|
|
self.steps += 1
|
|
|
|
def _transitions(self):
|
|
"""
|
|
Yield a single dictionary per high-level selected action
|
|
Fields:
|
|
obs: high-level state and mask at selection
|
|
action: chosen high-level action
|
|
reward: accumulated option reward
|
|
episode_start: whether the action was chosen at the episode start
|
|
value: the value estimate from the starting state
|
|
log_prob: the log_prob of the selected action from the starting state
|
|
done: whether the episode has ended
|
|
|
|
"""
|
|
yield {
|
|
'obs': self.obs,
|
|
'action': self.ch,
|
|
'reward': self.r.detach(),
|
|
'episode_start': self.episode_start,
|
|
'value': self.value.detach(),
|
|
'log_prob': self.log_prob.detach(),
|
|
'done': self.done,
|
|
}
|
|
|
|
def sample_hl(self, policy, discriminator):
|
|
"""
|
|
Args:
|
|
policy
|
|
discriminator: function with which to score rewards
|
|
Returns:
|
|
gen: iterable which samples high-level transitions from the environment
|
|
"""
|
|
self.discriminator = discriminator
|
|
return self.sample(policy)
|
|
|
|
class RenderOptions(LLOptions):
|
|
|
|
def _after_step(self):
|
|
"""
|
|
Render the environment after each low-level step
|
|
"""
|
|
super()._after_step()
|
|
self.env.render()
|
|
|
|
def close(self, *args, **kwargs):
|
|
"""
|
|
On 'close', close the environment
|
|
"""
|
|
self.env.close(*args, **kwargs)
|
|
|
|
def available_actions(env):
|
|
"""Return mask of available actions given current `env` state."""
|
|
valid = np.array([feasible(env, generate_plan(env, i), i) for i in range(len(ALL_OPTIONS))])
|
|
return valid
|
|
|
|
def target_velocity_plan(current_v: float, target_v: float, t: int, dt: float):
|
|
"""Smoothly target a velocity in a given number of steps"""
|
|
# for now, constant acceleration
|
|
a = (target_v - current_v) / (t * dt)
|
|
return a*np.ones((t,))
|
|
|
|
def generate_plan(env, i):
|
|
"""Generate input profile for high-level action `i`.
|
|
|
|
Args:
|
|
env (gym.Env): current environment state
|
|
i (int): high-level action `i`
|
|
Returns:
|
|
plan (np.array): length T array of acceleration values
|
|
"""
|
|
assert i < len(ALL_OPTIONS), "Invalid option index {i}"
|
|
target_v, t = ALL_OPTIONS[i]
|
|
current_v = env._env.state[env._agent, 1].item() # extract from env
|
|
plan = target_velocity_plan(current_v, target_v, t, env._env._dt)
|
|
assert len(plan) == t, "incorrect plan length"
|
|
return plan
|
|
|
|
def flatten_transitions(transitions):
|
|
return {
|
|
'obs': np.stack(list(t['obs'] for t in transitions), axis=0),
|
|
'next_obs': np.stack(list(t['next_obs'] for t in transitions), axis=0),
|
|
'acts': np.stack(list(t['acts'] for t in transitions), axis=0),
|
|
'dones': np.stack(list(t['dones'] for t in transitions), axis=0),
|
|
}
|
|
|
|
def train_discriminator(env, generator, discriminator, num_samples):
|
|
transitions = list(itertools.islice(env.sample_ll(generator), num_samples))
|
|
generator_samples = flatten_transitions(transitions)
|
|
discriminator.train_disc(gen_samples=generator_samples)
|
|
|
|
def train_generator(env, generator, discriminator, num_samples):
|
|
generator_samples = list(itertools.islice(env.sample_hl(generator, discriminator), num_samples+1))
|
|
|
|
generator.rollout_buffer.reset()
|
|
for s in generator_samples[:-1]:
|
|
generator.rollout_buffer.add(
|
|
obs=s['obs'],
|
|
action=s['action'].cpu(),
|
|
reward=s['reward'].cpu(),
|
|
episode_start=s['episode_start'],
|
|
value=s['value'],
|
|
log_prob=s['log_prob'],
|
|
)
|
|
|
|
generator.rollout_buffer.compute_returns_and_advantage(
|
|
last_values=generator_samples[-1]['value'],
|
|
dones=generator_samples[-1]['done'],
|
|
)
|
|
|
|
generator.train()
|
|
|
|
def train(expert_data, env_class=NRasterizedRandomAgent, env_settings={}, epochs=10, discrim_batch_size=32, generator_steps=2048, discount=0.99):
|
|
"""
|
|
Args:
|
|
expert_data: list of transitions
|
|
env_class: environment class
|
|
env_settings: environment settings
|
|
epochs: number of epochs to train for
|
|
discrim_batch_size: discriminator batch size
|
|
generator_steps: number of steps taken in generator
|
|
discount: discount factor
|
|
Returns:
|
|
generator (stable_baselines3.PPO): options policy
|
|
"""
|
|
env = env_class(**env_settings)
|
|
env.discount = discount
|
|
|
|
tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
|
|
tempdir_path = pathlib.Path(tempdir.name)
|
|
logger.configure(tempdir_path / "GAIL/")
|
|
print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
|
|
|
|
venv = make_vec_env(env_class, n_envs=1, env_kwargs=env_settings)
|
|
discriminator = adversarial.GAIL(
|
|
expert_data=expert_data,
|
|
expert_batch_size=discrim_batch_size,
|
|
discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
|
|
#discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
|
|
venv=venv, # unused
|
|
gen_algo=stable_baselines3.PPO("CnnPolicy", venv), # unused
|
|
)
|
|
|
|
generator = stable_baselines3.PPO(
|
|
OptionsCnnPolicy,
|
|
OptionsEnv(env),
|
|
verbose=1,
|
|
n_steps=generator_steps,
|
|
)
|
|
|
|
# PPO.train requires logger as set up in
|
|
# PPO._setup_learn (called by PPO.learn)
|
|
generator._logger = stable_baselines3.common.utils.configure_logger(
|
|
generator.verbose,
|
|
generator.tensorboard_log,
|
|
)
|
|
|
|
for _ in tqdm(range(epochs)):
|
|
train_discriminator(LLOptions(env), generator, discriminator, num_samples=discrim_batch_size)
|
|
train_generator(HLOptions(env), generator, discriminator, num_samples=generator_steps)
|
|
|
|
return generator
|
|
|
|
# %%
|
|
if __name__ == '__main__':
|
|
# %%
|
|
model_name = 'gail_options_image'
|
|
env_class = NRasterizedRandomAgent
|
|
env_settings = {'width': 36, 'height': 36, 'm_per_px': 2}
|
|
|
|
#env_class = NRasterized
|
|
#env_settings = {'agent': 51, 'width': 36, 'height': 36, 'm_per_px': 2}
|
|
files = ['../../../expert_data/DR_USA_Roundabout_FT0/track%04i/expert.pkl'%(i) for i in range(5)]
|
|
transitions=load_experts(files)
|
|
|
|
generator = train(
|
|
transitions,
|
|
env_class=env_class,
|
|
env_settings=env_settings,
|
|
epochs=10,
|
|
discrim_batch_size=32,
|
|
generator_steps=2048,
|
|
discount=0.99
|
|
)
|
|
|
|
generator.save(model_name)
|
|
|
|
# %%
|
|
model = stable_baselines3.PPO.load(model_name)
|
|
|
|
env = RenderOptions(NRasterizedRandomAgent(**env_args))
|
|
|
|
for s in env.sample_ll(model):
|
|
if s['dones']:
|
|
break
|
|
|
|
env.close(filestr='render/'+model_name)
|
|
|
|
# %% Tests
|
|
|
|
def test_ll_expert_data():
|
|
with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedAgent51w36h36mppx2.pkl", "rb") as f:
|
|
expert_trajectories = pickle.load(f)
|
|
expert_transitions = rollout.flatten_trajectories(expert_trajectories)
|
|
|
|
env = LLOptions(NRasterized(agent=51, width=36, height=36, m_per_px=2))
|
|
|
|
gen_transitions = list(itertools.islice(env.sample_ll(
|
|
policy=stable_baselines3.PPO(
|
|
OptionsCnnPolicy,
|
|
OptionsEnv(env),
|
|
verbose=1,
|
|
)
|
|
), 10))
|
|
gen_transitions = flatten_transitions(gen_transitions)
|
|
|
|
assert expert_transitions[:10].obs.shape == gen_transitions['obs'].shape
|
|
assert expert_transitions[:10].next_obs.shape == gen_transitions['next_obs'].shape
|
|
assert expert_transitions[:10].acts.shape == gen_transitions['acts'].shape
|
|
assert expert_transitions[:10].dones.shape == gen_transitions['dones'].shape
|
|
|
|
def test_ll_states():
|
|
env = NRasterized()
|
|
policy = stable_baselines3.PPO(
|
|
OptionsCnnPolicy,
|
|
OptionsEnv(env),
|
|
verbose=1,
|
|
)
|
|
llenv = LLOptions(env)
|
|
transitions = list(itertools.islice(llenv.sample_ll(policy=policy), 100))
|
|
|
|
env2 = NRasterized()
|
|
s2 = env2.reset()
|
|
for i, t in enumerate(transitions):
|
|
assert i == 0 or np.array_equal(t['obs'], transitions[i-1]['next_obs'])
|
|
assert np.array_equal(t['obs'], s2)
|
|
assert t['acts'].shape == (1,)
|
|
|
|
nexts2, _, done2, _ = env2.step(t['acts'])
|
|
assert np.array_equal(t['next_obs'], nexts2)
|
|
assert np.array_equal(t['dones'], done2)
|
|
|
|
if done2:
|
|
break
|
|
|
|
s2 = nexts2
|
|
|
|
def test_hl_transitions():
|
|
pass
|