import gym
from utils import wrappers
from gym.wrappers.time_limit import TimeLimit
from algos.test_policy import load_policy_and_env, run_policy
_, get_action = load_policy_and_env('./data/2')
env = TimeLimit(gym.make("PepperReachCam-v0", gui=True, dense=True),
max_episode_steps=100)
run_policy(env, get_action)
torch.autograd.profiler.profile(enabled=False)
env = TimeLimit(gym.make("PepperPush-v0", gui=True), max_episode_steps=100)
ac_kwargs = dict(hidden_sizes=[64, 64, 64], activation=nn.ReLU)
rb_kwargs = dict(size=1000000,
n_sampled_goal=4,
goal_selection_strategy='future')
logger_kwargs = dict(output_dir='data/push_su', exp_name='push_su')
model = SAC(env=env,
actor_critic=core_her.MLPActorCritic,
ac_kwargs=ac_kwargs,
replay_buffer=replay_buffer_her.ReplayBuffer,
rb_kwargs=rb_kwargs,
max_ep_len=100,
batch_size=256,
gamma=0.95,
lr=0.0003,
update_after=1000,
update_every=1,
logger_kwargs=logger_kwargs)
model.train(steps_per_epoch=2000, epochs=3000)
from algos.test_policy import load_policy_and_env, run_policy
_, get_action = load_policy_and_env('data/push_su')
run_policy(env, get_action)
dense=True,
head_motion=False),
max_episode_steps=100)
ac_kwargs = dict(hidden_sizes=[256, 256, 256], activation=nn.ReLU)
rb_kwargs = dict(size=1000000)
logger_kwargs = dict(output_dir='data/reach_su', exp_name='reach_su')
model = SAC(env=env,
actor_critic=core.MLPActorCritic,
ac_kwargs=ac_kwargs,
replay_buffer=replay_buffer.ReplayBuffer,
rb_kwargs=rb_kwargs,
max_ep_len=100,
batch_size=256,
gamma=0.95,
lr=0.0003,
ent_coef="auto",
update_after=1000,
update_every=1,
logger_kwargs=logger_kwargs)
model.train(steps_per_epoch=2000, epochs=1000)
from algos.test_policy import load_policy_and_env, run_policy
_, get_action = load_policy_and_env('data/reach_su')
run_policy(env, get_action)
import gym
import gym_pepper
from utils.wrappers import VisionWrapper, TorchifyWrapper
from gym.wrappers.time_limit import TimeLimit
from algos.test_policy import load_policy_and_env, run_policy
from cnn.cnn_0 import Net
env = TimeLimit(VisionWrapper(
TorchifyWrapper(gym.make("PepperReachCam-v0", gui=True, dense=True)), Net,
"trained/vision_0.pth"),
max_episode_steps=100)
_, get_action = load_policy_and_env("trained/0")
run_policy(env, get_action)
ac_kwargs = dict(hidden_sizes=[256, 256],
activation=nn.ReLU,
extractor_module=Extractor)
rb_kwargs = dict(size=40000)
logger_kwargs = dict(output_dir='data/reach_cam', exp_name='reach_cam')
model = SAC(env=env,
actor_critic=core_cam.MLPActorCritic,
ac_kwargs=ac_kwargs,
replay_buffer=replay_buffer_cam.ReplayBuffer,
rb_kwargs=rb_kwargs,
max_ep_len=100,
batch_size=256,
gamma=0.95,
lr=0.0003,
update_after=512,
update_every=512,
logger_kwargs=logger_kwargs,
use_gpu_buffer=True)
model.train(steps_per_epoch=1024, epochs=5000)
from algos.test_policy import load_policy_and_env, run_policy
_, get_action = load_policy_and_env('data/reach_cam', deterministic=True)
run_policy(env, get_action)
env = gym.make("FetchReach-v1")
ac_kwargs = dict(hidden_sizes=[64, 64], activation=nn.ReLU)
rb_kwargs = dict(size=1000000,
n_sampled_goal=4,
goal_selection_strategy='future')
logger_kwargs = dict(output_dir='data/fetch_reach_su', exp_name='fetch_reach_su')
model = SAC(env=env,
actor_critic=core_her.MLPActorCritic,
ac_kwargs=ac_kwargs,
replay_buffer=replay_buffer_her.ReplayBuffer,
rb_kwargs=rb_kwargs,
max_ep_len=100,
batch_size=256,
gamma=0.95,
lr=0.001,
ent_coef="auto",
update_after=1000,
update_every=1,
logger_kwargs=logger_kwargs)
model.train(steps_per_epoch=3000, epochs=10)
from algos.test_policy import load_policy_and_env, run_policy
_, get_action = load_policy_and_env('data/fetch_reach_su', deterministic=True)
run_policy(env, get_action)
import gym
from utils import wrappers
from gym.wrappers.time_limit import TimeLimit
from algos.test_policy import load_policy_and_env, run_policy
_, get_action = load_policy_and_env('trained/1')
env = TimeLimit(gym.make("PepperPush-v0", gui=True), max_episode_steps=100)
run_policy(env, get_action)