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main_trpo.py
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main_trpo.py
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import json
import numpy as np
import tensorflow as tf
from maml_rl.baselines import LinearFeatureBaseline
from maml_rl.metalearners import MetaLearner
from maml_rl.policies import CategoricalMLPPolicy, NormalMLPPolicy
from maml_rl.optimizers import ConjugateGradientOptimizer
from maml_rl.sampler import BatchSampler
def total_rewards(episodes_rewards, aggregation=tf.reduce_mean):
rewards = tf.math.reduce_mean(tf.stack([aggregation(tf.reduce_sum(rewards, axis=0))
for rewards in episodes_rewards], axis=0))
assert tf.rank(rewards) == 0
return rewards
def main(args):
continuous_actions = (args.env_name in ['AntVel-v1', 'AntDir-v1',
'AntPos-v0', 'HalfCheetahVel-v1',
'HalfCheetahDir-v1', '2DNavigation-v0'])
writer = tf.summary.create_file_writer('./logs/{0}'.format(args.output_folder))
save_folder = './saves/{0}'.format(args.output_folder)
if not os.path.exists(save_folder):
os.makedirs(save_folder)
with open(os.path.join(save_folder, 'config.json'), 'w') as f:
config = {k: v for (k, v) in vars(args).items() if k != 'device'}
config.update(device=args.device)
json.dump(config, f, indent=2)
sampler = BatchSampler(args.env_name,
batch_size=args.fast_batch_size,
num_workers=args.num_workers)
# Create policy for the given task
with tf.name_scope('policy') as scope:
if continuous_actions:
policy = NormalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
int(np.prod(sampler.envs.action_space.shape)),
hidden_sizes=(args.hidden_size,) * args.num_layers,
name=scope
)
else:
policy = CategoricalMLPPolicy(
int(np.prod(sampler.envs.observation_space.shape)),
sampler.envs.action_space.n,
hidden_sizes=(args.hidden_size,) * args.num_layers,
name=scope
)
baseline = LinearFeatureBaseline(int(np.prod(sampler.envs.observation_space.shape)))
optimizer = ConjugateGradientOptimizer(args.cg_damping,
args.cg_iters,
args.ls_backtrack_ratio,
args.ls_max_steps,
args.max_kl,
policy)
metalearner = MetaLearner(sampler,
policy,
baseline,
optimizer=optimizer,
gamma=args.gamma,
fast_lr=args.fast_lr,
tau=args.tau)
optimizer.setup(metalearner)
for batch in range(args.num_batches):
print(f"----------Batch number {batch+1}----------")
tasks = sampler.sample_tasks(num_tasks=args.meta_batch_size)
episodes = metalearner.sample(tasks,
first_order=args.first_order)
metalearner.step(episodes)
with writer.as_default():
return_before = total_rewards([ep.rewards for ep, _ in episodes])
return_after = total_rewards([ep.rewards for _, ep in episodes])
tf.summary.scalar('total_rewards/before_update', return_before, batch)
tf.summary.scalar('total_rewards/after_update', return_after, batch)
print(f"{batch+1}:: \t Before: {return_before} \t After: {return_after}")
writer.flush()
if (batch+1) % args.save_iters == 0:
# Save policy network
policy.save_weights(save_folder + f"/policy-{batch+1}", overwrite=True)
baseline.save_weights(save_folder + f"/baseline-{batch + 1}", overwrite=True)
print(f"Policy saved at iteration {batch+1}")
if __name__ == '__main__':
import argparse
import os
import multiprocessing as mp
parser = argparse.ArgumentParser(description='Reinforcement learning with '
'Model-Agnostic Meta-Learning (MAML)')
# General
parser.add_argument('--env-name', type=str,
help='name of the environment')
parser.add_argument('--gamma', type=float, default=0.95,
help='value of the discount factor gamma')
parser.add_argument('--tau', type=float, default=1.0,
help='value of the discount factor for GAE')
parser.add_argument('--first-order', action='store_true',
help='use the first-order approximation of MAML')
# Policy network (relu activation function)
parser.add_argument('--hidden-size', type=int, default=100,
help='number of hidden units per layer')
parser.add_argument('--num-layers', type=int, default=2,
help='number of hidden layers')
# Task-specific
parser.add_argument('--fast-batch-size', type=int, default=20,
help='batch size for each individual task')
parser.add_argument('--fast-lr', type=float, default=0.5,
help='learning rate for the 1-step gradient update of MAML')
# Optimization
parser.add_argument('--num-batches', type=int, default=200,
help='number of batches')
parser.add_argument('--meta-batch-size', type=int, default=40,
help='number of tasks per batch')
parser.add_argument('--max-kl', type=float, default=1e-2,
help='maximum value for the KL constraint in TRPO')
parser.add_argument('--cg-iters', type=int, default=10,
help='number of iterations of conjugate gradient')
parser.add_argument('--cg-damping', type=float, default=1e-5,
help='damping in conjugate gradient')
parser.add_argument('--ls-max-steps', type=int, default=15,
help='maximum number of iterations for line search')
parser.add_argument('--ls-backtrack-ratio', type=float, default=0.8,
help='maximum number of iterations for line search')
# Miscellaneous
parser.add_argument('--output-folder', type=str, default='maml',
help='name of the output folder')
parser.add_argument('--num-workers', type=int, default=mp.cpu_count() - 1,
help='number of workers for trajectories sampling')
parser.add_argument('--save-iters', type=int, default=10,
help='Number of iterations to pass so that the policy will be saved')
parser.add_argument('--device', type=str, default='cpu',
help='set the device (cpu or cuda)')
args = parser.parse_args()
# Create logs and saves folder if they don't exist
if not os.path.exists('./logs'):
os.makedirs('./logs')
if not os.path.exists('./saves'):
os.makedirs('./saves')
main(args)