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run_cartpole.py
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run_cartpole.py
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import numpy as np
import gym
import heapq
import argparse
import json
import os
from pendulum_gym import PendulumEnv
from cartpole_continuous import ContinuousCartPoleEnv
import torch
import scipy.stats as stats
from NB_dx_tf import neural_bays_dx_tf
from tf_models.constructor import construct_shallow_model, construct_shallow_cost_model, construct_model, construct_cost_model
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if __name__ == '__main__':
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
parser = argparse.ArgumentParser(description=None)
parser.add_argument('--env', default='CartPole-continuous', metavar='ENV',
help='env :[Pendulum-v0, CartPole-continuous, Pusher, Reacher]')
parser.add_argument('--with-reward', type=bool, default=False, metavar='NS',
help='predict with true rewards or not')
parser.add_argument('--sigma', type=float, default=1e-2, metavar='T', help='var for betas')
parser.add_argument('--sigma_n', type=float, default=1e-3, metavar='T', help='var for noise')
parser.add_argument('--hidden-dim-dx', type=int, default=200, metavar='NS')
parser.add_argument('--training-iter-dx', type=int, default=100, metavar='NS')
parser.add_argument('--hidden-dim-cost', type=int, default=200, metavar='NS')
parser.add_argument('--training-iter-cost', type=int, default=100, metavar='NS')
parser.add_argument('--predict_with_bias', type=bool, default=True, metavar='NS',
help='predict y with bias in BLR')
# CEM parameters
parser.add_argument('--num-trajs', type=int, default=500, metavar='NS',
help='number of sampling from params distribution')
parser.add_argument('--num-elites', type=int, default=50, metavar='NS', help='number of choosing best params')
parser.add_argument('--alpha', type=float, default=0.1, metavar='T',
help='Controls how much of the previous mean and variance is used for the next iteration.')
# parser.add_argument('--num-iters', type=int, default=100, metavar='NS',
# help='number of iterating the distribution params')
parser.add_argument('--plan-hor', type=int, default=30, metavar='NS', help='number of choosing best params')
parser.add_argument('--max-iters', type=int, default=5, metavar='NS', help='iteration of cem')
parser.add_argument('--epsilon', type=float, default=0.001, metavar='NS', help='threshold for cem iteration')
parser.add_argument('--var', type=float, default=1.0, metavar='T', help='var')
args = parser.parse_args()
print("current dir:", os.getcwd())
if 'CartPole-continuous' in args.env:
env = ContinuousCartPoleEnv()
elif 'Pendulum-v0' in args.env:
env = PendulumEnv()
elif "Pusher" in args.env:
env = PusherEnv()
else:
env = gym.make(args.env)
print('env', env)
slb = env.observation_space.low
sub = env.observation_space.high
alb = env.action_space.low
aub = env.action_space.high
# print(slb, sub, alb, aub)
obs_shape = env.observation_space.shape[0]
action_shape = len(env.action_space.sample())
dx_model = construct_shallow_model(obs_dim=obs_shape, act_dim=action_shape, hidden_dim=200, num_networks=1, num_elites=1)
my_dx = neural_bays_dx_tf(args, dx_model, "dx", obs_shape, sigma2=args.sigma**2, sigma_n2=args.sigma_n**2)
if not args.with_reward:
cost_model = construct_shallow_cost_model(obs_dim=obs_shape, act_dim=action_shape, hidden_dim=10, num_networks=1, num_elites=1)
my_cost = neural_bays_dx_tf(args, cost_model, "cost", 1, sigma2=args.sigma**2, sigma_n2=args.sigma_n**2)
cum_rewards = []
num_episode = 15
for episode in range(num_episode):
if args.with_reward:
from CEM_with import CEM
cem = CEM(env, args, my_dx, num_elites=args.num_elites, num_trajs=args.num_trajs, alpha=args.alpha)
else:
from CEM_without import CEM
cem = CEM(env, args, my_dx, my_cost, num_elites=args.num_elites, num_trajs=args.num_trajs, alpha=args.alpha)
state = torch.tensor(env.reset())
if 'Pendulum-v0' in args.env:
state = state.squeeze()
time_step = 0
done = False
my_dx.sample()
if not args.with_reward:
my_cost.sample()
num_steps = 200
cum_reward = 0
for _ in range(num_steps):
if episode == 0:
best_action = env.action_space.sample()
else:
best_action = cem.hori_planning(state)
if 'Pendulum-v0' in args.env:
best_action = np.array([best_action])
new_state, r, done, _ = env.step(best_action)
r = torch.tensor(r)
new_state = torch.tensor(new_state)
if 'Pendulum-v0' in args.env:
new_state = new_state.squeeze()
best_action = best_action.squeeze(0)
xu = torch.cat((state.double(), torch.tensor(best_action).double()))
my_dx.add_data(new_x=xu, new_y=new_state - state)
if not args.with_reward:
my_cost.add_data(new_x=xu, new_y=r)
cum_reward += r
state = new_state
print(episode, ': cumulative rewards', cum_reward.item())
cum_rewards.append([episode, cum_reward.tolist()])
my_dx.train(epochs=args.training_iter_dx)
my_dx.update_bays_reg()
if not args.with_reward:
my_cost.train(epochs=args.training_iter_cost)
my_cost.update_bays_reg()
np.savetxt('cartpole_log.txt', cum_rewards)
print(cum_rewards)