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
entropy_beta: factor for entropy boosted exploration
-----------------------------------------------
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
n_workers: manually set number of workers
update_itr: update global policy after several episodes
gamma: reward discount factor
save_interval: timesteps for saving the weights and plotting the results
mode: train or test
------------------------------------------------
"""
[docs]def atari(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=100,
gamma=0.9,
train_episodes=1000,
test_episodes=10,
save_interval=100,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params
[docs]def classic_control(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=100,
gamma=0.9,
train_episodes=1000,
test_episodes=10,
save_interval=100,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params
[docs]def box2d(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=20000,
gamma=0.9,
train_episodes=20000,
test_episodes=10,
save_interval=500,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params
[docs]def mujoco(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=100,
gamma=0.9,
train_episodes=1000,
test_episodes=10,
save_interval=100,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params
[docs]def robotics(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=100,
gamma=0.9,
train_episodes=1000,
test_episodes=10,
save_interval=100,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params
[docs]def dm_control(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=100,
gamma=0.9,
train_episodes=1000,
test_episodes=10,
save_interval=100,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params
[docs]def rlbench(env, default_seed=True):
if default_seed:
assert isinstance(env, list)
seed = np.arange(len(env)).tolist() # a list of seeds for each env
set_seed(seed, env) # reproducible
# for multi-threading
if isinstance(env, list): # judge if multiple envs are passed in for parallel computing
num_env = len(env) # number of envs passed in
env = env[0] # take one of the env as they are all the same
else:
num_env = 1
alg_params = dict(
entropy_beta=0.005
)
if alg_params.get('net_list') is None:
num_hidden_layer = 4 # number of hidden layers for the networks
hidden_dim = 64 # dimension of hidden layers for the networks
net_list2 = [] # networks list of networks list, each item for single thread/process
for _ in range(num_env + 1): # additional one for global
with tf.name_scope('AC'):
with tf.name_scope('Critic'):
critic = ValueNetwork(env.observation_space, hidden_dim_list=num_hidden_layer * [hidden_dim])
with tf.name_scope('Actor'):
actor = StochasticPolicyNetwork(env.observation_space, env.action_space,
hidden_dim_list=num_hidden_layer * [hidden_dim])
net_list = [actor, critic]
net_list2.append(net_list)
alg_params['net_list'] = net_list2
if alg_params.get('optimizers_list') is None:
a_lr, c_lr = 1e-3, 1e-3 # a_lr: learning rate of the actor; c_lr: learning rate of the critic
a_optimizer = tf.optimizers.RMSprop(a_lr, name='RMS_optimizer_actor')
c_optimizer = tf.optimizers.RMSprop(c_lr, name='RMS_optimizer_critic')
optimizers_list = [a_optimizer, c_optimizer]
alg_params['optimizers_list'] = optimizers_list
learn_params = dict(
max_steps=100,
gamma=0.9,
train_episodes=1000,
test_episodes=10,
save_interval=100,
update_itr=10,
n_workers=num_env
)
return alg_params, learn_params