from rlzoo.common.policy_networks import *
from rlzoo.common.utils import set_seed
"""
full list of algorithm parameters (alg_params)
-----------------------------------------------
net_list: a list of networks (value and policy) used in the algorithm, from common functions or customization
optimizers_list: a list of optimizers for all networks and differentiable variables
-----------------------------------------------
full list of learning parameters (learn_params)
-----------------------------------------------
train_episodes: total number of episodes for training
test_episodes: total number of episodes for testing
max_steps: maximum number of steps for one episode
save_interval: time steps for saving
mode: train or test
render: render each step
gamma: reward decay
-----------------------------------------------
"""
[docs]def atari(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params
[docs]def classic_control(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params
[docs]def box2d(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params
[docs]def mujoco(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params
[docs]def robotics(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params
[docs]def dm_control(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params
[docs]def rlbench(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict()
if alg_params.get('net_list') is None:
num_hidden_layer = 1 # number of hidden layers for the networks
hidden_dim = 32 # dimension of hidden layers for the networks
with tf.name_scope('PG'):
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
learning_rate = 0.02
policy_optimizer = tf.optimizers.Adam(learning_rate)
optimizers_list = [policy_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=200,
test_episodes=100,
max_steps=200,
save_interval=20,
gamma=0.95
)
return alg_params, learn_params