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rllab_experiment.py
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rllab_experiment.py
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# -*- coding: utf-8 -*-
import argparse
import os
import csv
# import platform
import gym
import torch
from torch import multiprocessing as mp
from model import ActorCritic
from optim import SharedRMSprop
from train import train
from test import test
from utils import Counter
from sac.misc.instrument import run_sac_experiment
from rllab.misc.instrument import VariantGenerator
parser = argparse.ArgumentParser(description='ACER')
parser.add_argument('--num-processes', type=int, default=6, metavar='N', help='Number of training async agents (does not include single validation agent)')
parser.add_argument('--T-max', type=int, default=500000, metavar='STEPS', help='Number of training steps')
parser.add_argument('--t-max', type=int, default=100, metavar='STEPS', help='Max number of forward steps for A3C before update')
parser.add_argument('--max-episode-length', type=int, default=500, metavar='LENGTH', help='Maximum episode length')
parser.add_argument('--hidden-size', type=int, default=32, metavar='SIZE', help='Hidden size of LSTM cell')
parser.add_argument('--model', type=str, metavar='PARAMS', help='Pretrained model (state dict)')
parser.add_argument('--on-policy', action='store_true', help='Use pure on-policy training (A3C)')
parser.add_argument('--memory-capacity', type=int, default=100000, metavar='CAPACITY', help='Experience replay memory capacity')
parser.add_argument('--replay-ratio', type=int, default=4, metavar='r', help='Ratio of off-policy to on-policy updates')
parser.add_argument('--replay-start', type=int, default=20000, metavar='EPISODES', help='Number of transitions to save before starting off-policy training')
parser.add_argument('--discount', type=float, default=0.99, metavar='γ', help='Discount factor')
parser.add_argument('--trace-decay', type=float, default=1, metavar='λ', help='Eligibility trace decay factor')
parser.add_argument('--trace-max', type=float, default=10, metavar='c', help='Importance weight truncation (max) value')
parser.add_argument('--trust-region', action='store_true', help='Use trust region')
parser.add_argument('--trust-region-decay', type=float, default=0.99, metavar='α', help='Average model weight decay rate')
parser.add_argument('--trust-region-threshold', type=float, default=1, metavar='δ', help='Trust region threshold value')
parser.add_argument('--reward-clip', action='store_true', help='Clip rewards to [-1, 1]')
parser.add_argument('--lr', type=float, default=0.0007, metavar='η', help='Learning rate')
parser.add_argument('--lr-decay', action='store_true', help='Linearly decay learning rate to 0')
parser.add_argument('--rmsprop-decay', type=float, default=0.99, metavar='α', help='RMSprop decay factor')
parser.add_argument('--batch-size', type=int, default=16, metavar='SIZE', help='Off-policy batch size')
parser.add_argument('--entropy-weight', type=float, default=0.0001, metavar='β', help='Entropy regularisation weight')
parser.add_argument('--max-gradient-norm', type=float, default=40, metavar='VALUE', help='Gradient L2 normalisation')
parser.add_argument('--evaluate', action='store_true', help='Evaluate only')
parser.add_argument('--evaluation-interval', type=int, default=25000, metavar='STEPS', help='Number of training steps between evaluations (roughly)')
parser.add_argument('--evaluation-episodes', type=int, default=10, metavar='N', help='Number of evaluation episodes to average over')
parser.add_argument('--render', action='store_true', help='Render evaluation agent')
parser.add_argument('--name', type=str, default='results', help='Save folder')
def run_acer(variant):
# BLAS setup
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'
# Setup
# args = parser.parse_args()
# Creating directories.
save_dir = os.path.join('results', 'results')
if not os.path.exists(save_dir):
os.makedirs(save_dir)
print(' ' * 26 + 'Options')
"""
# Saving parameters
with open(os.path.join(save_dir, 'params.txt'), 'w') as f:
for k, v in vars(args).items():
print(' ' * 26 + k + ': ' + str(v))
f.write(k + ' : ' + str(v) + '\n')
"""
# args.env = 'CartPole-v1' # TODO: Remove hardcoded environment when code is more adaptable
# mp.set_start_method(platform.python_version()[0] == '3' and 'spawn' or 'fork') # Force true spawning (not forking) if available
torch.manual_seed(variant['seed'])
T = Counter() # Global shared counter
# gym.logger.set_level(gym.logger.ERROR) # Disable Gym warnings
# Create shared network
env = gym.make(variant['env'])
shared_model = ActorCritic(env.observation_space, env.action_space, variant['hidden_size'])
shared_model.share_memory()
"""
if args.model and os.path.isfile(args.model):
# Load pretrained weights
shared_model.load_state_dict(torch.load(args.model))
"""
# Create average network
shared_average_model = ActorCritic(env.observation_space, env.action_space, variant['hidden_size'])
shared_average_model.load_state_dict(shared_model.state_dict())
shared_average_model.share_memory()
for param in shared_average_model.parameters():
param.requires_grad = False
# Create optimiser for shared network parameters with shared statistics
optimiser = SharedRMSprop(shared_model.parameters(), lr=variant['lr'], alpha=0.99)
optimiser.share_memory()
env.close()
fields = ['t', 'rewards', 'avg_steps', 'time']
with open(os.path.join(save_dir, 'test_results.csv'), 'w') as f:
writer = csv.writer(f)
writer.writerow(fields)
# Start validation agent
processes = []
p = mp.Process(target=test, args=(0, variant, T, shared_model))
p.start()
processes.append(p)
if not variant['evaluate']:
# Start training agents
for rank in range(1, variant['num-processes'] + 1):
p = mp.Process(target=train, args=(rank, variant, T, shared_model, shared_average_model, optimiser))
p.start()
print('Process ' + str(rank) + ' started')
processes.append(p)
# Clean up
for p in processes:
p.join()
COMMON_PARAMS = {
'seed': [2 + 10*i for i in range(5)],
'hidden_size': 32,
'num-processes': 6,
'T-max': 5000000,
't_max': 100,
'max-episode-length': 1000,
'on-policy': False,
'memory_capacity': 100000,
'replay_ratio': 4,
'replay_start': 20000,
'discount': 0.99,
'trace_decay': 1,
'trace_max': 10,
'trust_region': False,
'trust_region_decay': 0.99,
'trust_region_threshold': 1,
'reward_clip': False,
'lr': 0.0007,
'lr_decay': False,
'rmsprop_decay': 0.99,
'batch_size': 16,
'entropy_weight': 0.0001,
'max_gradient_norm': 40,
'evaluate': False, # ?
'evaluation-interval': 1000,
'evaluation-episodes': 1,
'render': False,
'name': 'results'
}
from rllab import config
config.DOCKER_IMAGE = "haarnoja/sac" # needs psutils
config.AWS_IMAGE_ID = "ami-a3a8b3da" # with docker already pulled
ENV_PARAMS = {
'env': ['Walker2d-v1']
}
def get_variants():
env_params = ENV_PARAMS
params = COMMON_PARAMS
params.update(env_params)
vg = VariantGenerator()
for key, val in params.items():
if isinstance(val, list):
vg.add(key, val)
else:
vg.add(key, [val])
return vg
def launch_experiments(variant_generator):
variants = variant_generator.variants()
num_experiments = len(variants)
print('Launching {} experiments.'.format(num_experiments))
exp_name = 'acer_test1'
for i, variant in enumerate(variants):
print("Experiment: {}/{}".format(i, num_experiments))
experiment_prefix = 'acer_baselines_final/' + variant['env'] + '/'
experiment_name = (
exp_name + '-' + str(i).zfill(2))
run_sac_experiment(
run_acer,
mode='local',
variant=variant,
exp_prefix=experiment_prefix,
exp_name=experiment_name,
n_parallel=1,
seed=variant['seed'],
terminate_machine=True,
snapshot_mode='last',
snapshot_gap=1000,
sync_s3_pkl=False,
)
def main():
# args = parse_args()
variant_generator = get_variants()
launch_experiments(variant_generator)
if __name__ == '__main__':
main()