from rlzoo.common.policy_networks import *
from rlzoo.common.value_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
replay_buffer_size: the size of buffer for storing explored samples
tau: soft update factor
-----------------------------------------------
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
explore_steps: for random action sampling in the beginning of training
mode: train or test mode
render: render each step
batch_size: update batch size
gamma: reward decay factor
noise_scale: range of action noise for exploration
noise_scale_decay: noise scale decay factor
-----------------------------------------------
"""
[docs]def classic_control(env, default_seed=True):
if default_seed:
# reproducible
seed = 2
set_seed(seed, env)
alg_params = dict(
replay_buffer_size=10000,
tau=0.01,
)
if alg_params.get('net_list') is None:
num_hidden_layer = 2 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
with tf.name_scope('DDPG'):
with tf.name_scope('Q_Net'):
q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net'):
target_q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Policy'):
target_policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [q_net, target_q_net, policy_net, target_policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
actor_lr = 1e-3
critic_lr = 2e-3
optimizers_list = [tf.optimizers.Adam(critic_lr), tf.optimizers.Adam(actor_lr)]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=100,
test_episodes=10,
max_steps=200,
save_interval=10,
explore_steps=500,
batch_size=32,
gamma=0.9,
noise_scale=1.,
noise_scale_decay=0.995
)
return alg_params, learn_params
[docs]def box2d(env, default_seed=True):
if default_seed:
# reproducible
seed = 2
set_seed(seed, env)
alg_params = dict(
replay_buffer_size=10000,
tau=0.01,
)
if alg_params.get('net_list') is None:
num_hidden_layer = 2 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
with tf.name_scope('DDPG'):
with tf.name_scope('Q_Net'):
q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net'):
target_q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Policy'):
target_policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [q_net, target_q_net, policy_net, target_policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
actor_lr = 1e-3
critic_lr = 2e-3
optimizers_list = [tf.optimizers.Adam(critic_lr), tf.optimizers.Adam(actor_lr)]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=100,
test_episodes=10,
max_steps=200,
save_interval=10,
explore_steps=500,
batch_size=32,
gamma=0.9,
noise_scale=1.,
noise_scale_decay=0.995
)
return alg_params, learn_params
[docs]def mujoco(env, default_seed=True):
if default_seed:
# reproducible
seed = 2
set_seed(seed, env)
alg_params = dict(
replay_buffer_size=10000,
tau=0.01,
)
if alg_params.get('net_list') is None:
num_hidden_layer = 2 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
with tf.name_scope('DDPG'):
with tf.name_scope('Q_Net'):
q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net'):
target_q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Policy'):
target_policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [q_net, target_q_net, policy_net, target_policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
actor_lr = 1e-3
critic_lr = 2e-3
optimizers_list = [tf.optimizers.Adam(critic_lr), tf.optimizers.Adam(actor_lr)]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=100,
test_episodes=10,
max_steps=200,
save_interval=10,
explore_steps=500,
batch_size=32,
gamma=0.9,
noise_scale=1.,
noise_scale_decay=0.995
)
return alg_params, learn_params
[docs]def robotics(env, default_seed=True):
if default_seed:
# reproducible
seed = 2
set_seed(seed, env)
alg_params = dict(
replay_buffer_size=10000,
tau=0.01,
)
if alg_params.get('net_list') is None:
num_hidden_layer = 2 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
with tf.name_scope('DDPG'):
with tf.name_scope('Q_Net'):
q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net'):
target_q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Policy'):
target_policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [q_net, target_q_net, policy_net, target_policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
actor_lr = 1e-3
critic_lr = 2e-3
optimizers_list = [tf.optimizers.Adam(critic_lr), tf.optimizers.Adam(actor_lr)]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=100,
test_episodes=10,
max_steps=200,
save_interval=10,
explore_steps=500,
batch_size=32,
gamma=0.9,
noise_scale=1.,
noise_scale_decay=0.995
)
return alg_params, learn_params
[docs]def dm_control(env, default_seed=True):
if default_seed:
# reproducible
seed = 2
set_seed(seed, env)
alg_params = dict(
replay_buffer_size=10000,
tau=0.01,
)
if alg_params.get('net_list') is None:
num_hidden_layer = 2 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
with tf.name_scope('DDPG'):
with tf.name_scope('Q_Net'):
q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net'):
target_q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Policy'):
target_policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [q_net, target_q_net, policy_net, target_policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
actor_lr = 1e-3
critic_lr = 2e-3
optimizers_list = [tf.optimizers.Adam(critic_lr), tf.optimizers.Adam(actor_lr)]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=100,
test_episodes=10,
max_steps=200,
save_interval=10,
explore_steps=500,
batch_size=32,
gamma=0.9,
noise_scale=1.,
noise_scale_decay=0.995
)
return alg_params, learn_params
[docs]def rlbench(env, default_seed=True):
if default_seed:
# reproducible
seed = 2
set_seed(seed, env)
alg_params = dict(
replay_buffer_size=1000,
tau=0.01,
)
if alg_params.get('net_list') is None:
num_hidden_layer = 2 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
with tf.name_scope('DDPG'):
with tf.name_scope('Q_Net'):
q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net'):
target_q_net = QNetwork(env.observation_space, env.action_space, num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Policy'):
target_policy_net = DeterministicPolicyNetwork(env.observation_space, env.action_space,
num_hidden_layer * [hidden_dim])
net_list = [q_net, target_q_net, policy_net, target_policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
actor_lr = 1e-3
critic_lr = 2e-3
optimizers_list = [tf.optimizers.Adam(critic_lr), tf.optimizers.Adam(actor_lr)]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
train_episodes=100,
test_episodes=10,
max_steps=200,
save_interval=10,
explore_steps=500,
batch_size=32,
gamma=0.9,
noise_scale=1.,
noise_scale_decay=0.995
)
return alg_params, learn_params