DDPG¶

Deep Deterministic Policy Gradient (DDPG)

Note

DDPG requires OpenMPI. If OpenMPI isn’t enabled, then DDPG isn’t imported into the stable_baselines module.

Warning

The DDPG model does not support stable_baselines.common.policies because it uses q-value instead of value estimation, as a result it must use its own policy models (see DDPG Policies).

Available Policies

`MlpPolicy`	Policy object that implements actor critic, using a MLP (2 layers of 64)
`LnMlpPolicy`	Policy object that implements actor critic, using a MLP (2 layers of 64), with layer normalisation
`CnnPolicy`	Policy object that implements actor critic, using a CNN (the nature CNN)
`LnCnnPolicy`	Policy object that implements actor critic, using a CNN (the nature CNN), with layer normalisation

Notes¶

Original paper: https://arxiv.org/abs/1509.02971
Baselines post: https://blog.openai.com/better-exploration-with-parameter-noise/
python -m stable_baselines.ddpg.main runs the algorithm for 1M frames = 10M timesteps on a Mujoco environment. See help (-h) for more options.

Can I use?¶

Recurrent policies: ❌
Multi processing: ✔️ (using MPI)
Gym spaces:

Space	Action	Observation
Discrete	❌	✔️
Box	✔️	✔️
MultiDiscrete	❌	✔️
MultiBinary	❌	✔️

Example¶

import gym
import numpy as np

from stable_baselines.ddpg.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines.ddpg.noise import NormalActionNoise, OrnsteinUhlenbeckActionNoise, AdaptiveParamNoiseSpec
from stable_baselines import DDPG

env = gym.make('MountainCarContinuous-v0')
env = DummyVecEnv([lambda: env])

# the noise objects for DDPG
n_actions = env.action_space.shape[-1]
param_noise = None
action_noise = OrnsteinUhlenbeckActionNoise(mean=np.zeros(n_actions), sigma=float(0.5) * np.ones(n_actions))

model = DDPG(MlpPolicy, env, verbose=1, param_noise=param_noise, action_noise=action_noise)
model.learn(total_timesteps=400000)
model.save("ddpg_mountain")

del model # remove to demonstrate saving and loading

model = DDPG.load("ddpg_mountain")

obs = env.reset()
while True:
    action, _states = model.predict(obs)
    obs, rewards, dones, info = env.step(action)
    env.render()

Parameters¶

class stable_baselines.ddpg.DDPG(policy, env, gamma=0.99, memory_policy=None, eval_env=None, nb_train_steps=50, nb_rollout_steps=100, nb_eval_steps=100, param_noise=None, action_noise=None, normalize_observations=False, tau=0.001, batch_size=128, param_noise_adaption_interval=50, normalize_returns=False, enable_popart=False, observation_range=(-5.0, 5.0), critic_l2_reg=0.0, return_range=(-inf, inf), actor_lr=0.0001, critic_lr=0.001, clip_norm=None, reward_scale=1.0, render=False, render_eval=False, memory_limit=None, buffer_size=50000, random_exploration=0.0, verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False)[source]¶

Deep Deterministic Policy Gradient (DDPG) model

DDPG: https://arxiv.org/pdf/1509.02971.pdf

Parameters:

policy – (DDPGPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, …)
env – (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
gamma – (float) the discount factor
memory_policy –
(ReplayBuffer) the replay buffer (if None, default to baselines.deepq.replay_buffer.ReplayBuffer)

Deprecated since version 2.6.0: This parameter will be removed in a future version
eval_env – (Gym Environment) the evaluation environment (can be None)
nb_train_steps – (int) the number of training steps
nb_rollout_steps – (int) the number of rollout steps
nb_eval_steps – (int) the number of evalutation steps
param_noise – (AdaptiveParamNoiseSpec) the parameter noise type (can be None)
action_noise – (ActionNoise) the action noise type (can be None)
param_noise_adaption_interval – (int) apply param noise every N steps
tau – (float) the soft update coefficient (keep old values, between 0 and 1)
normalize_returns – (bool) should the critic output be normalized
enable_popart – (bool) enable pop-art normalization of the critic output (https://arxiv.org/pdf/1602.07714.pdf), normalize_returns must be set to True.
normalize_observations – (bool) should the observation be normalized
batch_size – (int) the size of the batch for learning the policy
observation_range – (tuple) the bounding values for the observation
return_range – (tuple) the bounding values for the critic output
critic_l2_reg – (float) l2 regularizer coefficient
actor_lr – (float) the actor learning rate
critic_lr – (float) the critic learning rate
clip_norm – (float) clip the gradients (disabled if None)
reward_scale – (float) the value the reward should be scaled by
render – (bool) enable rendering of the environment
render_eval – (bool) enable rendering of the evalution environment
memory_limit –
(int) the max number of transitions to store, size of the replay buffer

Deprecated since version 2.6.0: Use buffer_size instead.
buffer_size – (int) the max number of transitions to store, size of the replay buffer
random_exploration – (float) Probability of taking a random action (as in an epsilon-greedy strategy) This is not needed for DDPG normally but can help exploring when using HER + DDPG. This hack was present in the original OpenAI Baselines repo (DDPG + HER)
verbose – (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
tensorboard_log – (str) the log location for tensorboard (if None, no logging)
_init_setup_model – (bool) Whether or not to build the network at the creation of the instance
policy_kwargs – (dict) additional arguments to be passed to the policy on creation
full_tensorboard_log – (bool) enable additional logging when using tensorboard WARNING: this logging can take a lot of space quickly

action_probability(observation, state=None, mask=None, actions=None, logp=False)[source]¶

If actions is None, then get the model’s action probability distribution from a given observation.

Depending on the action space the output is:

Discrete: probability for each possible action
Box: mean and standard deviation of the action output

However if actions is not None, this function will return the probability that the given actions are taken with the given parameters (observation, state, …) on this model. For discrete action spaces, it returns the probability mass; for continuous action spaces, the probability density. This is since the probability mass will always be zero in continuous spaces, see http://blog.christianperone.com/2019/01/ for a good explanation

Parameters:

observation – (np.ndarray) the input observation
state – (np.ndarray) The last states (can be None, used in recurrent policies)
mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
actions – (np.ndarray) (OPTIONAL) For calculating the likelihood that the given actions are chosen by the model for each of the given parameters. Must have the same number of actions and observations. (set to None to return the complete action probability distribution)
logp – (bool) (OPTIONAL) When specified with actions, returns probability in log-space. This has no effect if actions is None.

Returns:

(np.ndarray) the model’s (log) action probability

get_env()¶

returns the current environment (can be None if not defined)

Returns:	(Gym Environment) The current environment

get_parameter_list()[source]¶

Get tensorflow Variables of model’s parameters

This includes all variables necessary for continuing training (saving / loading).

Returns:	(list) List of tensorflow Variables

get_parameters()¶

Get current model parameters as dictionary of variable name -> ndarray.

Returns:	(OrderedDict) Dictionary of variable name -> ndarray of model’s parameters.

learn(total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name='DDPG', reset_num_timesteps=True, replay_wrapper=None)[source]¶

Return a trained model.

Parameters:

total_timesteps – (int) The total number of samples to train on
seed – (int) The initial seed for training, if None: keep current seed
callback – (function (dict, dict)) -> boolean function called at every steps with state of the algorithm. It takes the local and global variables. If it returns False, training is aborted.
log_interval – (int) The number of timesteps before logging.
tb_log_name – (str) the name of the run for tensorboard log
reset_num_timesteps – (bool) whether or not to reset the current timestep number (used in logging)

Returns:

(BaseRLModel) the trained model

classmethod load(load_path, env=None, custom_objects=None, **kwargs)[source]¶

Load the model from file

Parameters:

load_path – (str or file-like) the saved parameter location
env – (Gym Envrionment) the new environment to run the loaded model on (can be None if you only need prediction from a trained model)
custom_objects – (dict) Dictionary of objects to replace upon loading. If a variable is present in this dictionary as a key, it will not be deserialized and the corresponding item will be used instead. Similar to custom_objects in keras.models.load_model. Useful when you have an object in file that can not be deserialized.
kwargs – extra arguments to change the model when loading

load_parameters(load_path_or_dict, exact_match=True)¶

Load model parameters from a file or a dictionary

Dictionary keys should be tensorflow variable names, which can be obtained with get_parameters function. If exact_match is True, dictionary should contain keys for all model’s parameters, otherwise RunTimeError is raised. If False, only variables included in the dictionary will be updated.

This does not load agent’s hyper-parameters.

Warning

This function does not update trainer/optimizer variables (e.g. momentum). As such training after using this function may lead to less-than-optimal results.

Parameters:	load_path_or_dict – (str or file-like or dict) Save parameter location or dict of parameters as variable.name -> ndarrays to be loaded. exact_match – (bool) If True, expects load dictionary to contain keys for all variables in the model. If False, loads parameters only for variables mentioned in the dictionary. Defaults to True.

predict(observation, state=None, mask=None, deterministic=True)[source]¶

Get the model’s action from an observation

Parameters:	observation – (np.ndarray) the input observation state – (np.ndarray) The last states (can be None, used in recurrent policies) mask – (np.ndarray) The last masks (can be None, used in recurrent policies) deterministic – (bool) Whether or not to return deterministic actions.
Returns:	(np.ndarray, np.ndarray) the model’s action and the next state (used in recurrent policies)

pretrain(dataset, n_epochs=10, learning_rate=0.0001, adam_epsilon=1e-08, val_interval=None)¶

Pretrain a model using behavior cloning: supervised learning given an expert dataset.

NOTE: only Box and Discrete spaces are supported for now.

Parameters:	dataset – (ExpertDataset) Dataset manager n_epochs – (int) Number of iterations on the training set learning_rate – (float) Learning rate adam_epsilon – (float) the epsilon value for the adam optimizer val_interval – (int) Report training and validation losses every n epochs. By default, every 10th of the maximum number of epochs.
Returns:	(BaseRLModel) the pretrained model

save(save_path, cloudpickle=False)[source]¶

Save the current parameters to file

Parameters:	save_path – (str or file-like) The save location cloudpickle – (bool) Use older cloudpickle format instead of zip-archives.

set_env(env)¶

Checks the validity of the environment, and if it is coherent, set it as the current environment.

Parameters:	env – (Gym Environment) The environment for learning a policy

setup_model()[source]¶: Create all the functions and tensorflow graphs necessary to train the model

DDPG Policies¶

class stable_baselines.ddpg.MlpPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **_kwargs)[source]¶

Policy object that implements actor critic, using a MLP (2 layers of 64)

Parameters:

sess – (TensorFlow session) The current TensorFlow session
ob_space – (Gym Space) The observation space of the environment
ac_space – (Gym Space) The action space of the environment
n_env – (int) The number of environments to run
n_steps – (int) The number of steps to run for each environment
n_batch – (int) The number of batch to run (n_envs * n_steps)
reuse – (bool) If the policy is reusable or not
_kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction

action_ph¶: tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state¶: The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete¶: bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')¶

creates an actor object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the actor
Returns:	(TensorFlow Tensor) the output tensor

make_critic(obs=None, action=None, reuse=False, scope='qf')¶

creates a critic object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) action – (TensorFlow Tensor) The action placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the critic
Returns:	(TensorFlow Tensor) the output tensor

obs_ph¶: tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)¶

Returns the action probability for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) the action probability

processed_obs¶

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None)¶

Returns the policy for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) actions

value(obs, action, state=None, mask=None)¶

Returns the value for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment action – ([float] or [int]) The taken action state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) The associated value of the action

class stable_baselines.ddpg.LnMlpPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **_kwargs)[source]¶

Policy object that implements actor critic, using a MLP (2 layers of 64), with layer normalisation

Parameters:

sess – (TensorFlow session) The current TensorFlow session
ob_space – (Gym Space) The observation space of the environment
ac_space – (Gym Space) The action space of the environment
n_env – (int) The number of environments to run
n_steps – (int) The number of steps to run for each environment
n_batch – (int) The number of batch to run (n_envs * n_steps)
reuse – (bool) If the policy is reusable or not
_kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction

action_ph¶: tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state¶: The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete¶: bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')¶

creates an actor object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the actor
Returns:	(TensorFlow Tensor) the output tensor

make_critic(obs=None, action=None, reuse=False, scope='qf')¶

creates a critic object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) action – (TensorFlow Tensor) The action placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the critic
Returns:	(TensorFlow Tensor) the output tensor

obs_ph¶: tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)¶

Returns the action probability for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) the action probability

processed_obs¶

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None)¶

Returns the policy for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) actions

value(obs, action, state=None, mask=None)¶

Returns the value for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment action – ([float] or [int]) The taken action state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) The associated value of the action

class stable_baselines.ddpg.CnnPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **_kwargs)[source]¶

Policy object that implements actor critic, using a CNN (the nature CNN)

Parameters:

sess – (TensorFlow session) The current TensorFlow session
ob_space – (Gym Space) The observation space of the environment
ac_space – (Gym Space) The action space of the environment
n_env – (int) The number of environments to run
n_steps – (int) The number of steps to run for each environment
n_batch – (int) The number of batch to run (n_envs * n_steps)
reuse – (bool) If the policy is reusable or not
_kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction

action_ph¶: tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state¶: The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete¶: bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')¶

creates an actor object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the actor
Returns:	(TensorFlow Tensor) the output tensor

make_critic(obs=None, action=None, reuse=False, scope='qf')¶

creates a critic object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) action – (TensorFlow Tensor) The action placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the critic
Returns:	(TensorFlow Tensor) the output tensor

obs_ph¶: tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)¶

Returns the action probability for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) the action probability

processed_obs¶

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None)¶

Returns the policy for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) actions

value(obs, action, state=None, mask=None)¶

Returns the value for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment action – ([float] or [int]) The taken action state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) The associated value of the action

class stable_baselines.ddpg.LnCnnPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, **_kwargs)[source]¶

Policy object that implements actor critic, using a CNN (the nature CNN), with layer normalisation

Parameters:

sess – (TensorFlow session) The current TensorFlow session
ob_space – (Gym Space) The observation space of the environment
ac_space – (Gym Space) The action space of the environment
n_env – (int) The number of environments to run
n_steps – (int) The number of steps to run for each environment
n_batch – (int) The number of batch to run (n_envs * n_steps)
reuse – (bool) If the policy is reusable or not
_kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction

action_ph¶: tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state¶: The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete¶: bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')¶

creates an actor object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the actor
Returns:	(TensorFlow Tensor) the output tensor

make_critic(obs=None, action=None, reuse=False, scope='qf')¶

creates a critic object

Parameters:	obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder) action – (TensorFlow Tensor) The action placeholder (can be None for default placeholder) reuse – (bool) whether or not to resue parameters scope – (str) the scope name of the critic
Returns:	(TensorFlow Tensor) the output tensor

obs_ph¶: tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)¶

Returns the action probability for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) the action probability

processed_obs¶

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None)¶

Returns the policy for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) actions

value(obs, action, state=None, mask=None)¶

Returns the value for a single step

Parameters:	obs – ([float] or [int]) The current observation of the environment action – ([float] or [int]) The taken action state – ([float]) The last states (used in recurrent policies) mask – ([float]) The last masks (used in recurrent policies)
Returns:	([float]) The associated value of the action

Action and Parameters Noise¶

class stable_baselines.ddpg.AdaptiveParamNoiseSpec(initial_stddev=0.1, desired_action_stddev=0.1, adoption_coefficient=1.01)[source]¶

Implements adaptive parameter noise

Parameters:	initial_stddev – (float) the initial value for the standard deviation of the noise desired_action_stddev – (float) the desired value for the standard deviation of the noise adoption_coefficient – (float) the update coefficient for the standard deviation of the noise

adapt(distance)[source]¶

update the standard deviation for the parameter noise

Parameters:	distance – (float) the noise distance applied to the parameters

get_stats()[source]¶

return the standard deviation for the parameter noise

Returns:	(dict) the stats of the noise

class stable_baselines.ddpg.NormalActionNoise(mean, sigma)[source]¶

A gaussian action noise

Parameters:	mean – (float) the mean value of the noise sigma – (float) the scale of the noise (std here)

reset()¶: call end of episode reset for the noise

class stable_baselines.ddpg.OrnsteinUhlenbeckActionNoise(mean, sigma, theta=0.15, dt=0.01, initial_noise=None)[source]¶

A Ornstein Uhlenbeck action noise, this is designed to aproximate brownian motion with friction.

Based on http://math.stackexchange.com/questions/1287634/implementing-ornstein-uhlenbeck-in-matlab

Parameters:	mean – (float) the mean of the noise sigma – (float) the scale of the noise theta – (float) the rate of mean reversion dt – (float) the timestep for the noise initial_noise – ([float]) the initial value for the noise output, (if None: 0)

reset()[source]¶: reset the Ornstein Uhlenbeck noise, to the initial position

Custom Policy Network¶

Similarly to the example given in the examples page. You can easily define a custom architecture for the policy network:

import gym

from stable_baselines.ddpg.policies import FeedForwardPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import DDPG

# Custom MLP policy of two layers of size 16 each
class CustomDDPGPolicy(FeedForwardPolicy):
    def __init__(self, *args, **kwargs):
        super(CustomDDPGPolicy, self).__init__(*args, **kwargs,
                                           layers=[16, 16],
                                           layer_norm=False,
                                           feature_extraction="mlp")

# Create and wrap the environment
env = gym.make('Pendulum-v0')
env = DummyVecEnv([lambda: env])

model = DDPG(CustomDDPGPolicy, env, verbose=1)
# Train the agent
model.learn(total_timesteps=100000)