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_capacity: the size of buffer for storing explored samples
-----------------------------------------------
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
batch_size: udpate batchsize
explore_steps: for random action sampling in the beginning of training
update_itr: repeated updates for single step
policy_target_update_interval: delayed update for the policy network and target networks
reward_scale: value range of reward
save_interval: timesteps for saving the weights and plotting the results
mode: 'train' or 'test'
AUTO_ENTROPY: automatically udpating variable alpha for entropy
render: if true, visualize the environment
-----------------------------------------------
"""
[docs]def classic_control(env, default_seed=True):
if default_seed:
seed = 2
set_seed(seed, env) # reproducible
alg_params = dict(
replay_buffer_capacity=5e5,
)
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, default as the same for each layer here
with tf.name_scope('SAC'):
with tf.name_scope('Q_Net1'):
soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Q_Net2'):
soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net1'):
target_soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net2'):
target_soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim],
output_activation=None,
state_conditioned=True)
net_list = [soft_q_net1, soft_q_net2, target_soft_q_net1, target_soft_q_net2, policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
soft_q_lr, policy_lr, alpha_lr = 3e-4, 3e-4, 3e-4 # soft_q_lr: learning rate of the Q network; policy_lr: learning rate of the policy network; alpha_lr: learning rate of the variable alpha
soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
policy_optimizer = tf.optimizers.Adam(policy_lr)
alpha_optimizer = tf.optimizers.Adam(alpha_lr)
optimizers_list = [soft_q_optimizer1, soft_q_optimizer2, policy_optimizer, alpha_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=150,
batch_size=64,
explore_steps=200,
update_itr=3,
policy_target_update_interval=3,
reward_scale=1.,
AUTO_ENTROPY=True,
train_episodes=100,
test_episodes=10,
save_interval=10,
)
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(
replay_buffer_capacity=5e5,
)
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, default as the same for each layer here
with tf.name_scope('SAC'):
with tf.name_scope('Q_Net1'):
soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Q_Net2'):
soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net1'):
target_soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net2'):
target_soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim],
output_activation=None,
state_conditioned=True)
net_list = [soft_q_net1, soft_q_net2, target_soft_q_net1, target_soft_q_net2, policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
soft_q_lr, policy_lr, alpha_lr = 3e-4, 3e-4, 3e-4 # soft_q_lr: learning rate of the Q network; policy_lr: learning rate of the policy network; alpha_lr: learning rate of the variable alpha
soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
policy_optimizer = tf.optimizers.Adam(policy_lr)
alpha_optimizer = tf.optimizers.Adam(alpha_lr)
optimizers_list = [soft_q_optimizer1, soft_q_optimizer2, policy_optimizer, alpha_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=150,
batch_size=64,
explore_steps=200,
update_itr=3,
policy_target_update_interval=3,
reward_scale=1.,
AUTO_ENTROPY=True,
train_episodes=100,
test_episodes=10,
save_interval=10,
)
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(
replay_buffer_capacity=5e5,
)
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, default as the same for each layer here
with tf.name_scope('SAC'):
with tf.name_scope('Q_Net1'):
soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Q_Net2'):
soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net1'):
target_soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net2'):
target_soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim],
output_activation=None,
state_conditioned=True)
net_list = [soft_q_net1, soft_q_net2, target_soft_q_net1, target_soft_q_net2, policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
soft_q_lr, policy_lr, alpha_lr = 3e-4, 3e-4, 3e-4 # soft_q_lr: learning rate of the Q network; policy_lr: learning rate of the policy network; alpha_lr: learning rate of the variable alpha
soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
policy_optimizer = tf.optimizers.Adam(policy_lr)
alpha_optimizer = tf.optimizers.Adam(alpha_lr)
optimizers_list = [soft_q_optimizer1, soft_q_optimizer2, policy_optimizer, alpha_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=150,
batch_size=64,
explore_steps=200,
update_itr=3,
policy_target_update_interval=3,
reward_scale=1.,
AUTO_ENTROPY=True,
train_episodes=100,
test_episodes=10,
save_interval=10,
)
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(
replay_buffer_capacity=5e5,
)
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, default as the same for each layer here
with tf.name_scope('SAC'):
with tf.name_scope('Q_Net1'):
soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Q_Net2'):
soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net1'):
target_soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net2'):
target_soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim],
output_activation=None,
state_conditioned=True)
net_list = [soft_q_net1, soft_q_net2, target_soft_q_net1, target_soft_q_net2, policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
soft_q_lr, policy_lr, alpha_lr = 3e-4, 3e-4, 3e-4 # soft_q_lr: learning rate of the Q network; policy_lr: learning rate of the policy network; alpha_lr: learning rate of the variable alpha
soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
policy_optimizer = tf.optimizers.Adam(policy_lr)
alpha_optimizer = tf.optimizers.Adam(alpha_lr)
optimizers_list = [soft_q_optimizer1, soft_q_optimizer2, policy_optimizer, alpha_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=150,
batch_size=64,
explore_steps=200,
update_itr=3,
policy_target_update_interval=3,
reward_scale=1.,
AUTO_ENTROPY=True,
train_episodes=100,
test_episodes=10,
save_interval=10,
)
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(
replay_buffer_capacity=5e5,
)
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, default as the same for each layer here
with tf.name_scope('SAC'):
with tf.name_scope('Q_Net1'):
soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Q_Net2'):
soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net1'):
target_soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net2'):
target_soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim],
output_activation=None,
state_conditioned=True)
net_list = [soft_q_net1, soft_q_net2, target_soft_q_net1, target_soft_q_net2, policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
soft_q_lr, policy_lr, alpha_lr = 3e-4, 3e-4, 3e-4 # soft_q_lr: learning rate of the Q network; policy_lr: learning rate of the policy network; alpha_lr: learning rate of the variable alpha
soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
policy_optimizer = tf.optimizers.Adam(policy_lr)
alpha_optimizer = tf.optimizers.Adam(alpha_lr)
optimizers_list = [soft_q_optimizer1, soft_q_optimizer2, policy_optimizer, alpha_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=150,
batch_size=64,
explore_steps=200,
update_itr=3,
policy_target_update_interval=3,
reward_scale=1.,
AUTO_ENTROPY=True,
train_episodes=100,
test_episodes=10,
save_interval=10,
)
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(
replay_buffer_capacity=5e5,
)
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, default as the same for each layer here
with tf.name_scope('SAC'):
with tf.name_scope('Q_Net1'):
soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Q_Net2'):
soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net1'):
target_soft_q_net1 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Target_Q_Net2'):
target_soft_q_net2 = QNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Policy'):
policy_net = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim],
output_activation=None,
state_conditioned=True)
net_list = [soft_q_net1, soft_q_net2, target_soft_q_net1, target_soft_q_net2, policy_net]
alg_params['net_list'] = net_list
if alg_params.get('optimizers_list') is None:
soft_q_lr, policy_lr, alpha_lr = 3e-4, 3e-4, 3e-4 # soft_q_lr: learning rate of the Q network; policy_lr: learning rate of the policy network; alpha_lr: learning rate of the variable alpha
soft_q_optimizer1 = tf.optimizers.Adam(soft_q_lr)
soft_q_optimizer2 = tf.optimizers.Adam(soft_q_lr)
policy_optimizer = tf.optimizers.Adam(policy_lr)
alpha_optimizer = tf.optimizers.Adam(alpha_lr)
optimizers_list = [soft_q_optimizer1, soft_q_optimizer2, policy_optimizer, alpha_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=150,
batch_size=64,
explore_steps=200,
update_itr=3,
policy_target_update_interval=3,
reward_scale=1.,
AUTO_ENTROPY=True,
train_episodes=100,
test_episodes=10,
save_interval=10,
)
return alg_params, learn_params