def main(argv):
log_flags(FLAGS)
f = functools.partial(EnvWrap,
reward_scale=FLAGS.reward_scale,
action_repeat=FLAGS.action_repeat)
vec_env = SubprocVecEnv([f for _ in range(8)])
eval_env = EnvWrap(reward_scale=1,
action_repeat=FLAGS.action_repeat,
rm_done=False)
config_class = A2CTrainerConfig
train_class = A2CTrainer
if FLAGS.visualize:
tconf = config_class.from_json(
os.path.join(OUTPUT_DIR, FLAGS.config_path))
trainer = train_class(vec_env, eval_env, tconf, OUTPUT_DIR)
trainer.load_checkpoint(os.path.join(OUTPUT_DIR,
FLAGS.checkpoint_path))
eval_return, eval_steps = trainer.evaluate(render=True)
print(f'EvalReturn {eval_return:.1f} Steps {eval_steps:.1f}')
else: # Train
tconf = config_class.from_flags(FLAGS)
tconf.to_json(os.path.join(OUTPUT_DIR, FLAGS.config_path))
trainer = train_class(vec_env, eval_env, tconf, OUTPUT_DIR)
trainer.train()
trainer.save_checkpoint(os.path.join(OUTPUT_DIR,
FLAGS.checkpoint_path))
vec_env.close()
eval_env.close()
def multi_fps(n_workers):
vec_env = SubprocVecEnv([make_env_fn for _ in range(n_workers)])
start_time = time.time()
steps = 0
for episode in range(500):
vec_env.reset()
for idx, act in enumerate(ref_actions[reset_step:]):
acts = np.tile(act, (n_workers, 1))
obs, rew, done, info = vec_env.step(acts)
steps += 1
elapsed = time.time() - start_time
fps = steps / elapsed
print(f'{n_workers}-worker FPS: {fps} EffectiveFPS: {fps*n_workers}')
vec_env.close()
def main(argv):
""" Trains a model through backward RL. """
ref_actions = np.load(os.path.join(DATA_DIR, FLAGS.ref_actions_path))
clip_name, start_step = parse_path(FLAGS.ref_actions_path)
make_env_fn = lambda: RefTrackingEnv(
clip_name, ref_actions, start_step, reset_step=0)
vec_env = SubprocVecEnv([make_env_fn for _ in range(FLAGS.num_workers)])
eval_env = make_env_fn()
config_class = SACTrainerConfig
train_class = SACTrainer
if FLAGS.visualize:
tconf = config_class.from_json(FLAGS.config_path)
trainer = train_class(vec_env, env, tconf, OUTPUT_DIR)
trainer.load_checkpoint(os.path.join(OUTPUT_DIR,
FLAGS.checkpoint_path))
env.visualize(trainer.policy, device='cpu')
else:
tconf = config_class.from_flags(FLAGS)
tconf.to_json(os.path.join(OUTPUT_DIR, FLAGS.config_path))
trainer = train_class(vec_env, eval_env, tconf, OUTPUT_DIR)
# Generate the curriculum
for idx in range(len(ref_actions)):
reset_step = len(ref_actions) - (idx + 1)
# Modify the environments to reflect the new reset_step
vec_env.set_attr('reset_step', reset_step)
vec_env.reset()
eval_env.reset_step = reset_step
eval_env.reset()
target_return = eval_env.ref_returns[reset_step]
print(
f'Curriculum Task {idx}: reset_step {reset_step} target_return {target_return:.3f}'
)
trainer.train(target_return)
trainer.save_checkpoint(
os.path.join(OUTPUT_DIR, FLAGS.checkpoint_path))
vec_env.close()
eval_env.close()
def main(argv):
log_flags(FLAGS)
f = functools.partial(
RefTrackingEnv,
reward_type=FLAGS.reward_type,
clip_name=FLAGS.clip_name,
reward_scale=FLAGS.reward_scale,
always_init_at_clip_start=FLAGS.always_init_at_clip_start,
use_ref_observables=FLAGS.use_ref_observables,
)
vec_env = SubprocVecEnv([f for _ in range(FLAGS.workers)],
norm_obs=FLAGS.norm_obs)
eval_env = RefTrackingEnv(
reward_type=FLAGS.reward_type,
clip_name=FLAGS.clip_name,
always_init_at_clip_start=True,
ghost_offset=None, #1.0 if FLAGS.visualize else None,
use_ref_observables=FLAGS.use_ref_observables,
)
config_class = SACTrainerConfig
train_class = SACTrainer
if FLAGS.visualize:
tconf = config_class.from_json(
os.path.join(OUTPUT_DIR, FLAGS.config_path))
tconf.buffer_size = 100 # Only for visualization purposes
trainer = train_class(vec_env, eval_env, tconf, OUTPUT_DIR)
trainer.load_checkpoint(os.path.join(OUTPUT_DIR,
FLAGS.checkpoint_path))
eval_env.visualize(trainer.model, device='cpu', deterministic=False)
else: # Train
tconf = config_class.from_flags(FLAGS)
tconf.to_json(os.path.join(OUTPUT_DIR, FLAGS.config_path))
trainer = train_class(vec_env, eval_env, tconf, OUTPUT_DIR)
trainer.train()
trainer.save_checkpoint(os.path.join(OUTPUT_DIR,
FLAGS.checkpoint_path))
vec_env.close()
eval_env.close()
# Check set_attr for reset_step is correct
make_env_fn = lambda: RefTrackingEnv(
'CMU_069_02', ref_actions, 0, reset_step=len(ref_actions) - 2)
vec_env = SubprocVecEnv([make_env_fn for _ in range(2)])
# Change the reset step and make sure we don't get an epsiode termination until expected
vec_env.set_attr('reset_step', reset_step)
vec_env.reset()
for idx, act in enumerate(ref_actions[reset_step:]):
acts = np.tile(act, (2, 1))
obs, rew, done, info = vec_env.step(acts)
# Ensure we're not done until the last step
if idx < len(ref_actions[reset_step:]) - 1:
assert not np.any(done)
# Make sure we're done on the last step
assert np.all(done)
vec_env.close()
# Check for correctness
for _ in range(2):
obs = env.reset()
# Check that the reset is working properly and we get the expected sequences
# of observations and rewards from executing the reference actions.
rewards = []
for idx, act in enumerate(ref_actions[reset_step:]):
obs, rew, done, info = env.step(act)
# Ensure we're not done until the last step
if idx < len(ref_actions[reset_step:]) - 1:
assert not done
rewards.append(rew)
# Make sure the observations match what's expected
assert np.all(env.ref_observations[reset_step + idx + 1] -