DQN

Deep Q Network (DQN) and its extensions (Double-DQN, Dueling-DQN, Prioritized Experience Replay).

Warning

The DQN model does not support stable_baselines.common.policies, as a result it must use its own policy models (see DQN Policies).

Available Policies

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

Notes

Note

By default, the DQN class has double q learning and dueling extensions enabled. See Issue #406 for disabling dueling. To disable double-q learning, you can change the default value in the constructor.

Can I use?

  • Recurrent policies: ❌
  • Multi processing: ❌
  • Gym spaces:
Space Action Observation
Discrete ✔️ ✔️
Box ✔️
MultiDiscrete ✔️
MultiBinary ✔️

Example

import gym

from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines.deepq.policies import MlpPolicy
from stable_baselines import DQN

env = gym.make('CartPole-v1')

model = DQN(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)
model.save("deepq_cartpole")

del model # remove to demonstrate saving and loading

model = DQN.load("deepq_cartpole")

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

With Atari:

from stable_baselines.common.atari_wrappers import make_atari
from stable_baselines.deepq.policies import MlpPolicy, CnnPolicy
from stable_baselines import DQN

env = make_atari('BreakoutNoFrameskip-v4')

model = DQN(CnnPolicy, env, verbose=1)
model.learn(total_timesteps=25000)
model.save("deepq_breakout")

del model # remove to demonstrate saving and loading

model = DQN.load("deepq_breakout")

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

Parameters

class stable_baselines.deepq.DQN(policy, env, gamma=0.99, learning_rate=0.0005, buffer_size=50000, exploration_fraction=0.1, exploration_final_eps=0.02, train_freq=1, batch_size=32, double_q=True, learning_starts=1000, target_network_update_freq=500, prioritized_replay=False, prioritized_replay_alpha=0.6, prioritized_replay_beta0=0.4, prioritized_replay_beta_iters=None, prioritized_replay_eps=1e-06, param_noise=False, n_cpu_tf_sess=None, verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False, seed=None)[source]

The DQN model class. DQN paper: https://arxiv.org/abs/1312.5602 Dueling DQN: https://arxiv.org/abs/1511.06581 Double-Q Learning: https://arxiv.org/abs/1509.06461 Prioritized Experience Replay: https://arxiv.org/abs/1511.05952

Parameters:
  • policy – (DQNPolicy 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) discount factor
  • learning_rate – (float) learning rate for adam optimizer
  • buffer_size – (int) size of the replay buffer
  • exploration_fraction – (float) fraction of entire training period over which the exploration rate is annealed
  • exploration_final_eps – (float) final value of random action probability
  • train_freq – (int) update the model every train_freq steps. set to None to disable printing
  • batch_size – (int) size of a batched sampled from replay buffer for training
  • double_q – (bool) Whether to enable Double-Q learning or not.
  • learning_starts – (int) how many steps of the model to collect transitions for before learning starts
  • target_network_update_freq – (int) update the target network every target_network_update_freq steps.
  • prioritized_replay – (bool) if True prioritized replay buffer will be used.
  • prioritized_replay_alpha – (float)alpha parameter for prioritized replay buffer. It determines how much prioritization is used, with alpha=0 corresponding to the uniform case.
  • prioritized_replay_beta0 – (float) initial value of beta for prioritized replay buffer
  • prioritized_replay_beta_iters – (int) number of iterations over which beta will be annealed from initial value to 1.0. If set to None equals to max_timesteps.
  • prioritized_replay_eps – (float) epsilon to add to the TD errors when updating priorities.
  • param_noise – (bool) Whether or not to apply noise to the parameters of the policy.
  • 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
  • full_tensorboard_log – (bool) enable additional logging when using tensorboard WARNING: this logging can take a lot of space quickly
  • seed – (int) Seed for the pseudo-random generators (python, numpy, tensorflow). If None (default), use random seed. Note that if you want completely deterministic results, you must set n_cpu_tf_sess to 1.
  • n_cpu_tf_sess – (int) The number of threads for TensorFlow operations If None, the number of cpu of the current machine will be used.
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, log_interval=100, tb_log_name='DQN', reset_num_timesteps=True, replay_wrapper=None)[source]

Return a trained model.

Parameters:
  • total_timesteps – (int) The total number of samples to train on
  • 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)

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
set_random_seed(seed)
Parameters:seed – (int) Seed for the pseudo-random generators. If None, do not change the seeds.
setup_model()[source]

Create all the functions and tensorflow graphs necessary to train the model

DQN Policies

class stable_baselines.deepq.MlpPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, obs_phs=None, dueling=True, **_kwargs)[source]

Policy object that implements DQN policy, 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
  • obs_phs – (TensorFlow Tensor, TensorFlow Tensor) a tuple containing an override for observation placeholder and the processed observation placeholder respectivly
  • dueling – (bool) if true double the output MLP to compute a baseline for action scores
  • _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.

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 – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
Returns:

(np.ndarray 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, deterministic=True)

Returns the q_values for a single step

Parameters:
  • obs – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

(np.ndarray int, np.ndarray float, np.ndarray float) actions, q_values, states

class stable_baselines.deepq.LnMlpPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, obs_phs=None, dueling=True, **_kwargs)[source]

Policy object that implements DQN policy, 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
  • obs_phs – (TensorFlow Tensor, TensorFlow Tensor) a tuple containing an override for observation placeholder and the processed observation placeholder respectivly
  • dueling – (bool) if true double the output MLP to compute a baseline for action scores
  • _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.

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 – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
Returns:

(np.ndarray 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, deterministic=True)

Returns the q_values for a single step

Parameters:
  • obs – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

(np.ndarray int, np.ndarray float, np.ndarray float) actions, q_values, states

class stable_baselines.deepq.CnnPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, obs_phs=None, dueling=True, **_kwargs)[source]

Policy object that implements DQN policy, 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
  • obs_phs – (TensorFlow Tensor, TensorFlow Tensor) a tuple containing an override for observation placeholder and the processed observation placeholder respectivly
  • dueling – (bool) if true double the output MLP to compute a baseline for action scores
  • _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.

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 – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
Returns:

(np.ndarray 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, deterministic=True)

Returns the q_values for a single step

Parameters:
  • obs – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

(np.ndarray int, np.ndarray float, np.ndarray float) actions, q_values, states

class stable_baselines.deepq.LnCnnPolicy(sess, ob_space, ac_space, n_env, n_steps, n_batch, reuse=False, obs_phs=None, dueling=True, **_kwargs)[source]

Policy object that implements DQN policy, 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
  • obs_phs – (TensorFlow Tensor, TensorFlow Tensor) a tuple containing an override for observation placeholder and the processed observation placeholder respectivly
  • dueling – (bool) if true double the output MLP to compute a baseline for action scores
  • _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.

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 – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
Returns:

(np.ndarray 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, deterministic=True)

Returns the q_values for a single step

Parameters:
  • obs – (np.ndarray float or int) The current observation of the environment
  • state – (np.ndarray float) The last states (used in recurrent policies)
  • mask – (np.ndarray float) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

(np.ndarray int, np.ndarray float, np.ndarray float) actions, q_values, states

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.deepq.policies import FeedForwardPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import DQN

# Custom MLP policy of two layers of size 32 each
class CustomDQNPolicy(FeedForwardPolicy):
    def __init__(self, *args, **kwargs):
        super(CustomDQNPolicy, self).__init__(*args, **kwargs,
                                           layers=[32, 32],
                                           layer_norm=False,
                                           feature_extraction="mlp")

# Create and wrap the environment
env = gym.make('LunarLander-v2')
env = DummyVecEnv([lambda: env])

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