def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'Pendulum-v0'
pickle_path = '../gpirl/notebooks/plots/rllab_trpo_trainig/itr_112.pkl'
# pickle_path = 'data/acrobat_data_rllab_trpo/exp_1/itr_800.pkl'
iter_data = joblib.load(pickle_path)
env = GymEnv(env_name)
max_r = 1
while True:
o = env.reset()
disc_r = 0
r_sum = 0
done = False
i = 0
while not done:
env.render()
a, _ = iter_data['policy'].get_action(o)
o, r, done, _ = env.step(a)
# s = [np.arccos(o[0]), np.arccos(o[1])]
# r = -np.cos(s[0]) - np.cos(s[1] + s[0])
disc_r += r * 0.99**(500 - i)
r_sum += r
i += 1
# max_r = r
print("disc_r : {} , sum_r : {}".format(disc_r, r_sum))
print("last x : {}".format(o[0]))
print("-------------------------")
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'Cartpole-v3'
pickle_path = 'data/Cartpole_v3_data_rllab_TRPO/exp_1/itr_1200.pkl'
# pickle_path = 'data/acrobat_data_rllab_trpo/exp_1/itr_800.pkl'
iter_data = joblib.load(pickle_path)
env = GymEnv(env_name)
max_r = 1
while True:
o = env.reset()
disc_r = 0
r_sum = 0
done = False
i = 0
print("stable point : {}".format(env.env._stable_x))
while not done:
env.render()
a, _ = iter_data['policy'].get_action(o)
o, r, done, _ = env.step(a)
# s = [np.arccos(o[0]), np.arccos(o[1])]
# r = -np.cos(s[0]) - np.cos(s[1] + s[0])
disc_r += r * 0.99**(500 - i)
r_sum += r
i += 1
# max_r = r
print("disc_r : {} , sum_r : {}".format(disc_r, r_sum))
print("last x : {}".format(o[0]))
print("-------------------------")
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'LunarLanderContinuous-v3'
env = GymEnv(env_name)
policy = DeterministicMLPPolicy(env_spec=env.spec, hidden_sizes=(64, 64))
es = OUStrategy(env_spec=env.spec)
qf = ContinuousMLPQFunction(env_spec=env.spec)
algo = DDPG(
env=env,
policy=policy,
es=es,
qf=qf,
batch_size=32,
max_path_length=350,
epoch_length=350,
min_pool_size=350,
n_epochs=600,
discount=0.99,
scale_reward=1.0/140.0,
qf_learning_rate=1e-3,
policy_learning_rate=1e-4,
# Uncomment both lines (this and the plot parameter below) to enable plotting
# plot=True,
)
data_path = 'data/%s_data_rllab_%s/%s/'%(env_name.replace('-', '_'),
str(algo.__class__.__name__),
exp_name)
os.makedirs(data_path, exist_ok=True)
logger.set_snapshot_dir(data_path)
algo.train()
logger.set_snapshot_dir(None)
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'LunarLanderContinuous-v3'
env = GymEnv(env_name)
itr_num = 400
episode_length = 400
while True:
o = env.reset()
disc_r = 0
r_sum = 0
done = False
i = 0
pickle_path = '../gpirl/notebooks_lunarlander/plots/gpirl_400_iter_post_trainig/itr_{}.pkl'.format(
itr_num)
# pickle_path = 'data/LunarLanderContinuous_v3_data_rllab_PPO/exp_1/itr_{}.pkl'.format(itr_num)
iter_data = joblib.load(pickle_path)
while i < episode_length:
env.render()
a, _ = iter_data['policy'].get_action(o)
o, r, done, _ = env.step(a)
# s = [np.arccos(o[0]), np.arccos(o[1])]
# r = -np.cos(s[0]) - np.cos(s[1] + s[0])
disc_r += r * 0.99**(500 - i)
r_sum += r
i += 1
if done:
break
# max_r = r
print("disc_r : {} , sum_r : {}".format(disc_r, r_sum))
print("last x : {}".format(o[0]))
print("iterations : {}".format(i))
print("-------------------------")
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'LunarLanderContinuous-v3'
env = GymEnv(env_name)
max_r = 1
itr_num = 0
itr_inc = 30
init_max_trials = 6
norm_max_trials = 4
max_iters = 400
trail_num = 1
while itr_num < 211:
o = env.reset()
if itr_num < 1:
max_trials = init_max_trials
else:
max_trials = norm_max_trials
disc_r = 0
r_sum = 0
done = False
i = 0
# pickle_path = '../gpirl/notebooks_lunarlander/plots/gpirl_400_iter_post_trainig/itr_{}.pkl'.format(itr_num)
pickle_path = 'data/LunarLanderContinuous_v3_data_rllab_PPO/exp_1/itr_{}.pkl'.format(
itr_num)
iter_data = joblib.load(pickle_path)
while i < max_iters:
env.render()
a, _ = iter_data['policy'].get_action(o)
o, r, done, _ = env.step(a)
# s = [np.arccos(o[0]), np.arccos(o[1])]
# r = -np.cos(s[0]) - np.cos(s[1] + s[0])
disc_r += r * 0.99**(500 - i)
r_sum += r
i += 1
if done:
break
# max_r = r
print("disc_r : {} , sum_r : {}".format(disc_r, r_sum))
print("last x : {}".format(o[0]))
print("iterations : {}".format(i))
print("-------------------------")
if trail_num % max_trials == 0:
if itr_num < 1:
trail_num = norm_max_trials
itr_num += itr_inc
print("**************** itr number : {} **********************".
format(itr_num))
trail_num += 1
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'Acrobot-v2'
env = GymEnv(env_name)
policy = GaussianMLPPolicy(env_spec=env, hidden_sizes=(64, 64))
algo = PPO(env=env,
policy=policy,
n_itr=1500,
batch_size=8000,
max_path_length=1000,
discount=0.95,
store_paths=True,
entropy_weight=ent_wt,
baseline=LinearFeatureBaseline(env_spec=env))
data_path = 'data/acrobat_data_rllab_ppo/%s/' % exp_name
os.makedirs(data_path, exist_ok=True)
logger.set_snapshot_dir(data_path)
algo.train()
logger.set_snapshot_dir(None)
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'LunarLanderContinuous-v3'
env = GymEnv(env_name)
policy = GaussianMLPPolicy(env_spec=env, hidden_sizes=(64, 64))
baseline = GaussianMLPBaseline(env_spec=env)
algo = PPO(env=env,
policy=policy,
n_itr=1500,
batch_size=8000,
max_path_length=1000,
discount=0.99,
store_paths=True,
entropy_weight=ent_wt,
baseline=baseline)
data_path = 'data/%s_data_rllab_%s/%s/' % (env_name.replace(
'-', '_'), str(algo.__class__.__name__), exp_name)
os.makedirs(data_path, exist_ok=True)
logger.set_snapshot_dir(data_path)
algo.train()
logger.set_snapshot_dir(None)
def main(exp_name, ent_wt=1.0):
register_custom_envs()
env_name = 'Acrobot-v2'
pickle_path = 'data/acrobat_data_moded_reward_ppo/exp_1/itr_800.pkl'
# pickle_path = 'data/acrobat_data_rllab_trpo/exp_1/itr_800.pkl'
iter_data = joblib.load(pickle_path)
env = GymEnv(env_name)
max_r = 1
while True:
o = env.reset()
disc_r=0
done=False
i=0
# print("New episode!")
while not done:
env.render()
a, _ = iter_data['policy'].get_action(o)
o, r, done, _ = env.step(a)
s = [np.arccos(o[0]) , np.arccos(o[1])]
# r = -np.cos(s[0]) - np.cos(s[1] + s[0])
disc_r += r * 0.99 ** (1000-i)
i += 1
# max_r = r
print("disc_r : {}".format(disc_r))
from __future__ import print_function
from inverse_rl.envs import register_custom_envs
import sys, gym, time
import numpy as np
from pyglet.window import key as ks
import datetime
import pandas as pd
import matplotlib.pyplot as plt
import os
#
# Test yourself as a learning agent! Pass environment name as a command-line argument, for example:
#
# python keyboard_agent.py SpaceInvadersNoFrameskip-v4
#
keyboard = ks.KeyStateHandler()
register_custom_envs()
env = gym.make(
'LunarLanderContinuous-v3' if len(sys.argv) < 2 else sys.argv[1])
Kp = 0.1
Kt = 0.2
gamma = 0.9
save_path = "data/lunarlander_demo/"
ACTIONS = env.action_space
RESET_ACTION = np.asarray([0., 0.])
SKIP_CONTROL = 0 # Use previous control decision SKIP_CONTROL times, that's how you
# can test what skip is still usable.
# print("ACTION high low : {} , {}".format(env.action_space.high, env.action_space.low))
human_wants_restart = False
human_sets_pause = False
state_cols = ["state_" + str(i) for i in range(env.observation_space.shape[0])]
