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run_noisy.py
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run_noisy.py
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import argparse
import os.path as osp
import numpy as np
from env import Env
from os import path
from util import log
import math
import json
def argparser(args=None):
prs = argparse.ArgumentParser()
add = prs.add_argument
add('--env_id', default='Hopper-v3')
add('--envE_id', default=None)
add('--seed', type=int, default=0)
add('--expr_dir', default='expr_noisy')
add('--pi_step', type=int, default=1000)
add('--d_step', type=int, default=1000)
add('--alg', type=str, choices=['sac', 'gail', 'dac', 'airl',
'fairl', 'rairl', 'mdirl'], default='gail')
add('--policy', type=str)
add('--batch_size', type=int, default=256)
add('--q', type=float, default=2.)
add('--k', type=float, default=1.)
add('--k2', type=float, default=1.)
add('--reg_lvl', type=int, default=0)
add('--lr', type=float, default=5e-4)
add('--alpha', type=float, default=1.)
add('--alphaT', type=float, default=-1)
add('--gamma', type=float, default=0.99)
add('--gp_coeff', type=float, default=1e-2)
add('--num_steps', type=int, default=int(1e6))
add('--replay_size', type=int, default=int(5e5))
add('--record_intvl', type=int, default=5000)
add('--save_intvl', type=int, default=-1)
add('--burnin_steps', type=int, default=10000)
add('--num_demo', type=int, default=1000)
add('--noise_lvl', type=float, default=1e-3)
add('--tag', type=str, default='')
return prs.parse_args(args=args)
def sci(i):
absi = np.abs(i)
if 1 <= absi <= 99 or absi == 0.0: return str(int(i))
elif 0.1 <= absi < 1: return "{:.1f}".format(i)
p = int(math.floor(np.log(absi) / np.log(10)))
return str(int(np.sign(i))*int(round((absi/(10**p)))))+'e'+str(p)
def get_expr_name(args):
expr_name = f'{args.alg}.'
expr_name += args.env_id.split('-')[0]
if args.alg != 'sac' and args.envE_id and args.envE_id != args.env_id:
expr_name += ('-' + args.envE_id.split("-")[0])
if args.q == 1.0 or args.q == 2.0:
q_str = str(int(args.q))
else:
q_str = "{:.1f}".format(args.q)
expr_name += f'.q_{q_str}.k_{sci(args.k)}.k2_{sci(args.k2)}'
if args.alg == 'mdirl':
if args.alphaT < 0.:
args.alphaT = args.alpha
expr_name += f'.α_{sci(args.alpha)}'
else:
expr_name += f'.α0_{sci(args.alpha)}.αT_{sci(args.alphaT)}'
if args.alg != 'sac':
expr_name += f".n_{args.num_demo}"
expr_name += f'.noise_{sci(args.noise_lvl)}'
expr_name += f'.seed_{args.seed}'
if args.tag != '':
expr_name += ("."+args.tag)
return expr_name
def main(args):
nh = 100
if "Ant-v3" == args.env_id:
from env import AntEnv
env = AntEnv(args.env_id)
eval_env = AntEnv(args.env_id)
if args.alg == 'sac' or args.envE_id is None:
args.envE_id = args.env_id
envE = env
else:
envE = AntEnv(args.envE_id)
ns = 27
na = np.prod(env.A.shape)
else:
env = Env(args.env_id)
eval_env = Env(args.env_id)
if args.alg == 'sac' or args.envE_id is None:
args.envE_id = args.env_id
envE = env
else:
envE = Env(args.envE_id)
ns = np.prod(env.S.shape)
na = np.prod(env.A.shape)
if na == 1: args.policy = 'diag_gaussian'
expr_name = get_expr_name(args)
expr_path = path.join(args.expr_dir, expr_name)
from const import set_const
set_const(args.q, args.k, args.k2, args.reg_lvl,
args.alpha, args.alphaT,
args.gamma, ns, na, nh, env.A.high, args.gp_coeff)
from util.rnd import set_seed
set_seed(args.seed, env, envE)
from util.rms import init
init()
from util.pd import set_pd
set_pd(args.policy)
log.configure(expr_path, formats=['stdout', 'csv', 'tb'])
if args.policy == 'gaussian':
from nn.pi import GaussianPolicyValue
π = GaussianPolicyValue()
elif args.policy == 'diag_gaussian':
from nn.pi import DiagGaussianPolicyValue
π = DiagGaussianPolicyValue()
elif args.policy == 'diag_gaussian2':
from nn.pi import DiagGaussianPolicyValue2
π = DiagGaussianPolicyValue2()
from loss import set_πv
set_πv(π.vars,π.q1_vars,π.q2_vars,
π.fwd,π.fwdq1,π.fwdq2,π.fwdq1t,π.fwdq2t)
from data import ReplayBuffer
if args.alg == 'sac':
buf = ReplayBuffer(args.replay_size, int(1e4), π, env, ns, na)
D = expert_dataset = None
else:
if args.alg == 'gail':
from nn.disc import GAIL
D = GAIL()
elif args.alg == 'dac':
from nn.disc import DAC
D = DAC()
elif args.alg == 'airl':
from nn.disc import AIRL
D = AIRL(π)
elif args.alg == 'fairl':
from nn.disc import FAIRL
D = FAIRL()
elif args.alg == 'rairl':
if args.policy == 'gaussian':
from nn.disc import RAIRLGaussian
D = RAIRLGaussian(π)
elif args.policy == 'diag_gaussian':
from nn.disc import RAIRLDiagGaussian
D = RAIRLDiagGaussian(π)
elif args.policy == 'diag_gaussian2':
from nn.disc import RAIRLDiagGaussian2
D = RAIRLDiagGaussian2(π)
elif args.alg == 'mdirl':
if args.policy == 'gaussian':
from nn.disc import MirrorDescentGaussian
D = MirrorDescentGaussian(π)
elif args.policy == 'diag_gaussian':
from nn.disc import MirrorDescentDiagGaussian
D = MirrorDescentDiagGaussian(π)
elif args.policy == 'diag_gaussian2':
from nn.disc import MirrorDescentDiagGaussian2
D = MirrorDescentDiagGaussian2(π)
from loss import set_d
set_d(D.vars, D.loss, D.rwd)
buf = ReplayBuffer(args.replay_size, int(1e4), π, env, ns, na, D.intype)
from data import get_trj
num_demo = args.num_demo
env_name = args.envE_id.split("-")[0]
expert_traj_path = f'data_noisy/trj{num_demo}.{env_name}.{args.noise_lvl}.npz'
expert_dataset = get_trj(args.env_id, expert_traj_path, D.intype)
from train import train
train(args.alg, env, eval_env, π, buf,
D, expert_dataset, args.batch_size,
π_step=args.pi_step,
d_step=args.d_step,
lr_π=args.lr,
lr_d=args.lr,
max_steps=args.num_steps,
expr_path=expr_path,
record_intvl=args.record_intvl,
save_intvl=args.save_intvl,
burnin_steps=args.burnin_steps)
log.close()
param_dict = {}
for idx, var in enumerate(π.vars):
param_dict[f'pi_{idx}'] = var.numpy()
from util.rms import r_rms
for idx, var in enumerate(r_rms.vars):
param_dict[f'r_rms_{idx}'] = var.numpy()
if D is not None:
for idx, var in enumerate(D.vars):
param_dict[f'd_{idx}'] = var.numpy()
param_path = osp.join(expr_path, 'param.npz')
with open(param_path, 'wb') as f:
np.savez(f, **param_dict)
if __name__ == '__main__':
args = argparser()
main(args)