def view_policy_ddpg():
env = DummyVecEnv([lambda: EnvHandler(make_env())])
view(env,
seed=None,
total_timesteps=10000,
reward_scale=1.0,
render=True,
render_eval=False,
noise_type=None,
normalize_returns=False,
normalize_observations=False,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-3,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=50,
nb_eval_steps=100,
nb_save_epochs=None,
batch_size=64,
tau=0.01,
action_range=(-250.0, 250.0),
observation_range=(-5.0, 5.0),
eval_env=None,
load_path="./checkpoints/00007",
save_dir=None,
param_noise_adaption_interval=50)
def train_ddpg():
env = SubprocVecEnv([lambda: EnvHandler(make_env()) for _ in range(1)])
#env = SubprocVecEnv([lambda: EnvHandler(make_env(env_no=0)), lambda: EnvHandler(make_env(env_no=1))])
learn(env=env,
seed=None,
total_timesteps=1e5,
nb_epochs=None,
nb_epoch_cycles=10,
nb_rollout_steps=100,
reward_scale=1.0,
render=False,
render_eval=False,
noise_type='ou-param_0.2',
normalize_returns=False,
normalize_observations=False,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-3,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=50,
nb_eval_steps=100,
batch_size=64,
tau=0.01,
eval_env=None,
param_noise_adaption_interval=50,
nb_save_epochs=1,
save_dir=".",
load_path=None)
def train_td3():
env = SubprocVecEnv([lambda: EnvHandler(make_env()) for _ in range(1)])
learn(env,
total_timesteps=1e6,
nb_epochs=None,
nb_rollout_steps=100,
max_ep_len=250,
reward_scale=1.0,
render=False,
render_eval=False,
noise_type='adaptive-param_0.2',
normalize_returns=False,
normalize_observations=True,
actor_lr=1e-4,
critic_lr=1e-3,
popart=False,
gamma=0.99,
clip_norm=None,
start_steps=10000,
nb_train_steps=50,
nb_eval_steps=100,
nb_log_steps=100,
nb_save_steps=None,
batch_size=64,
polyak=0.01,
action_range=(-250.0, 250.0),
observation_range=(-5.0, 5.0),
target_noise=0.2,
noise_clip=0.5,
policy_delay=2,
load_path=None,
save_dir=None)
def view_policy_ppo():
env = DummyVecEnv([lambda: EnvHandler(make_env())])
view(env=env,
episodes=100,
total_timesteps=1000000,
nsteps=200,
nminibatches=1,
cliprange=0.2,
ent_coef=0.0,
lam=0.95,
gamma=0.99,
noptepochs=4,
save_interval=100,
save_dir=".",
load_path="./checkpoints/00500",
normalize_observations=False,
normalize_returns=False)
def train_ppo():
env = SubprocVecEnv([lambda: EnvHandler(make_env())])
learn(env=env,
eval_env=None,
total_timesteps=3e7,
nsteps=128,
nminibatches=1,
cliprange=0.2,
ent_coef=0.01,
vf_coef=0.5,
lam=0.95,
gamma=0.99,
noptepochs=4,
lr=2.5e-4,
save_interval=100,
save_dir=".",
load_path=None,
normalize_observations=False,
normalize_returns=False)