DQN

Deep Q Network (DQN) builds on Fitted Q-Iteration (FQI) and make use of different tricks to stabilize the learning with neural networks: it uses a replay buffer, a target network and gradient clipping.

Available Policies

MlpPolicy

alias of DQNPolicy

CnnPolicy

Policy class for DQN when using images as input.

MultiInputPolicy

Policy class for DQN when using dict observations as input.

Notes

Note

This implementation provides only vanilla Deep Q-Learning and has no extensions such as Double-DQN, Dueling-DQN and Prioritized Experience Replay.

Can I use?

  • Recurrent policies: ❌

  • Multi processing: ✔️

  • Gym spaces:

Space

Action

Observation

Discrete

✔️

✔️

Box

❌

✔️

MultiDiscrete

❌

✔️

MultiBinary

❌

✔️

Dict

❌

✔️️

Example

This example is only to demonstrate the use of the library and its functions, and the trained agents may not solve the environments. Optimized hyperparameters can be found in RL Zoo repository.

import gym

from stable_baselines3 import DQN

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

model = DQN("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=10000, log_interval=4)
model.save("dqn_cartpole")

del model # remove to demonstrate saving and loading

model = DQN.load("dqn_cartpole")

obs = env.reset()
while True:
    action, _states = model.predict(obs, deterministic=True)
    obs, reward, done, info = env.step(action)
    env.render()
    if done:
      obs = env.reset()

Results

Atari Games

The complete learning curves are available in the associated PR #110.

How to replicate the results?

Clone the rl-zoo repo:

git clone https://github.com/DLR-RM/rl-baselines3-zoo
cd rl-baselines3-zoo/

Run the benchmark (replace $ENV_ID by the env id, for instance BreakoutNoFrameskip-v4):

python train.py --algo dqn --env $ENV_ID --eval-episodes 10 --eval-freq 10000

Plot the results:

python scripts/all_plots.py -a dqn -e Pong Breakout -f logs/ -o logs/dqn_results
python scripts/plot_from_file.py -i logs/dqn_results.pkl -latex -l DQN

Parameters

class stable_baselines3.dqn.DQN(policy, env, learning_rate=0.0001, buffer_size=1000000, learning_starts=50000, batch_size=32, tau=1.0, gamma=0.99, train_freq=4, gradient_steps=1, replay_buffer_class=None, replay_buffer_kwargs=None, optimize_memory_usage=False, target_update_interval=10000, exploration_fraction=0.1, exploration_initial_eps=1.0, exploration_final_eps=0.05, max_grad_norm=10, tensorboard_log=None, policy_kwargs=None, verbose=0, seed=None, device='auto', _init_setup_model=True)[source]

Deep Q-Network (DQN)

Paper: https://arxiv.org/abs/1312.5602, https://www.nature.com/articles/nature14236 Default hyperparameters are taken from the Nature paper, except for the optimizer and learning rate that were taken from Stable Baselines defaults.

Parameters:

  • policy (Union[str, Type[DQNPolicy]]) – The policy model to use (MlpPolicy, CnnPolicy, …)

  • env (Union[Env, VecEnv, str]) – The environment to learn from (if registered in Gym, can be str)

  • learning_rate (Union[float, Callable[[float], float]]) – The learning rate, it can be a function of the current progress remaining (from 1 to 0)

  • buffer_size (int) – size of the replay buffer

  • learning_starts (int) – how many steps of the model to collect transitions for before learning starts

  • batch_size (int) – Minibatch size for each gradient update

  • tau (float) – the soft update coefficient (“Polyak update”, between 0 and 1) default 1 for hard update

  • gamma (float) – the discount factor

  • train_freq (Union[int, Tuple[int, str]]) – Update the model every train_freq steps. Alternatively pass a tuple of frequency and unit like (5, "step") or (2, "episode").

  • gradient_steps (int) – How many gradient steps to do after each rollout (see train_freq) Set to -1 means to do as many gradient steps as steps done in the environment during the rollout.

  • replay_buffer_class (Optional[Type[ReplayBuffer]]) – Replay buffer class to use (for instance HerReplayBuffer). If None, it will be automatically selected.

  • replay_buffer_kwargs (Optional[Dict[str, Any]]) – Keyword arguments to pass to the replay buffer on creation.

  • optimize_memory_usage (bool) – Enable a memory efficient variant of the replay buffer at a cost of more complexity. See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195

  • target_update_interval (int) – update the target network every target_update_interval environment steps.

  • exploration_fraction (float) – fraction of entire training period over which the exploration rate is reduced

  • exploration_initial_eps (float) – initial value of random action probability

  • exploration_final_eps (float) – final value of random action probability

  • max_grad_norm (float) – The maximum value for the gradient clipping

  • tensorboard_log (Optional[str]) – the log location for tensorboard (if None, no logging)

  • policy_kwargs (Optional[Dict[str, Any]]) – additional arguments to be passed to the policy on creation

  • verbose (int) – Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages

  • seed (Optional[int]) – Seed for the pseudo random generators

  • device (Union[device, str]) – Device (cpu, cuda, …) on which the code should be run. Setting it to auto, the code will be run on the GPU if possible.

  • _init_setup_model (bool) – Whether or not to build the network at the creation of the instance

collect_rollouts(env, callback, train_freq, replay_buffer, action_noise=None, learning_starts=0, log_interval=None)

Collect experiences and store them into a ReplayBuffer.

Parameters:

  • env (VecEnv) – The training environment

  • callback (BaseCallback) – Callback that will be called at each step (and at the beginning and end of the rollout)

  • train_freq (TrainFreq) – How much experience to collect by doing rollouts of current policy. Either TrainFreq(<n>, TrainFrequencyUnit.STEP) or TrainFreq(<n>, TrainFrequencyUnit.EPISODE) with <n> being an integer greater than 0.

  • action_noise (Optional[ActionNoise]) – Action noise that will be used for exploration Required for deterministic policy (e.g. TD3). This can also be used in addition to the stochastic policy for SAC.

  • learning_starts (int) – Number of steps before learning for the warm-up phase.

  • replay_buffer (ReplayBuffer) –

  • log_interval (Optional[int]) – Log data every log_interval episodes

Return type:

RolloutReturn

Returns:

get_env()

Returns the current environment (can be None if not defined).

Return type:

Optional[VecEnv]

Returns:

The current environment

get_parameters()

Return the parameters of the agent. This includes parameters from different networks, e.g. critics (value functions) and policies (pi functions).

Return type:

Dict[str, Dict]

Returns:

Mapping of from names of the objects to PyTorch state-dicts.

get_vec_normalize_env()

Return the VecNormalize wrapper of the training env if it exists.

Return type:

Optional[VecNormalize]

Returns:

The VecNormalize env.

learn(total_timesteps, callback=None, log_interval=4, tb_log_name='DQN', reset_num_timesteps=True, progress_bar=False)[source]

Return a trained model.

Parameters:

  • total_timesteps (int) – The total number of samples (env steps) to train on

  • callback (Union[None, Callable, List[BaseCallback], BaseCallback]) – callback(s) called at every step with state of the algorithm.

  • log_interval (int) – The number of timesteps before logging.

  • tb_log_name (str) – the name of the run for TensorBoard logging

  • reset_num_timesteps (bool) – whether or not to reset the current timestep number (used in logging)

  • progress_bar (bool) – Display a progress bar using tqdm and rich.

Return type:

TypeVar(SelfDQN, bound= DQN)

Returns:

the trained model

classmethod load(path, env=None, device='auto', custom_objects=None, print_system_info=False, force_reset=True, **kwargs)

Load the model from a zip-file. Warning: load re-creates the model from scratch, it does not update it in-place! For an in-place load use set_parameters instead.

Parameters:

  • path (Union[str, Path, BufferedIOBase]) – path to the file (or a file-like) where to load the agent from

  • env (Union[Env, VecEnv, None]) – the new environment to run the loaded model on (can be None if you only need prediction from a trained model) has priority over any saved environment

  • device (Union[device, str]) – Device on which the code should run.

  • custom_objects (Optional[Dict[str, Any]]) – 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.

  • print_system_info (bool) – Whether to print system info from the saved model and the current system info (useful to debug loading issues)

  • force_reset (bool) – Force call to reset() before training to avoid unexpected behavior. See https://github.com/DLR-RM/stable-baselines3/issues/597

  • kwargs – extra arguments to change the model when loading

Return type:

TypeVar(SelfBaseAlgorithm, bound= BaseAlgorithm)

Returns:

new model instance with loaded parameters

load_replay_buffer(path, truncate_last_traj=True)

Load a replay buffer from a pickle file.

Parameters:

  • path (Union[str, Path, BufferedIOBase]) – Path to the pickled replay buffer.

  • truncate_last_traj (bool) – When using HerReplayBuffer with online sampling: If set to True, we assume that the last trajectory in the replay buffer was finished (and truncate it). If set to False, we assume that we continue the same trajectory (same episode).

Return type:

None

property logger: Logger

Getter for the logger object.

predict(observation, state=None, episode_start=None, deterministic=False)[source]

Overrides the base_class predict function to include epsilon-greedy exploration.

Parameters:

  • observation (Union[ndarray, Dict[str, ndarray]]) – the input observation

  • state (Optional[Tuple[ndarray, ...]]) – The last states (can be None, used in recurrent policies)

  • episode_start (Optional[ndarray]) – The last masks (can be None, used in recurrent policies)

  • deterministic (bool) – Whether or not to return deterministic actions.

Return type:

Tuple[ndarray, Optional[Tuple[ndarray, ...]]]

Returns:

the model’s action and the next state (used in recurrent policies)

save(path, exclude=None, include=None)

Save all the attributes of the object and the model parameters in a zip-file.

Parameters:

  • path (Union[str, Path, BufferedIOBase]) – path to the file where the rl agent should be saved

  • exclude (Optional[Iterable[str]]) – name of parameters that should be excluded in addition to the default ones

  • include (Optional[Iterable[str]]) – name of parameters that might be excluded but should be included anyway

Return type:

None

save_replay_buffer(path)

Save the replay buffer as a pickle file.

Parameters:

path (Union[str, Path, BufferedIOBase]) – Path to the file where the replay buffer should be saved. if path is a str or pathlib.Path, the path is automatically created if necessary.

Return type:

None

set_env(env, force_reset=True)

Checks the validity of the environment, and if it is coherent, set it as the current environment. Furthermore wrap any non vectorized env into a vectorized checked parameters: - observation_space - action_space

Parameters:
Return type:

None

set_logger(logger)

Setter for for logger object. :rtype: None

Warning

When passing a custom logger object, this will overwrite tensorboard_log and verbose settings passed to the constructor.

set_parameters(load_path_or_dict, exact_match=True, device='auto')

Load parameters from a given zip-file or a nested dictionary containing parameters for different modules (see get_parameters).

Parameters:

  • load_path_or_iter – Location of the saved data (path or file-like, see save), or a nested dictionary containing nn.Module parameters used by the policy. The dictionary maps object names to a state-dictionary returned by torch.nn.Module.state_dict().

  • exact_match (bool) – If True, the given parameters should include parameters for each module and each of their parameters, otherwise raises an Exception. If set to False, this can be used to update only specific parameters.

  • device (Union[device, str]) – Device on which the code should run.

Return type:

None

set_random_seed(seed=None)

Set the seed of the pseudo-random generators (python, numpy, pytorch, gym, action_space)

Parameters:

seed (Optional[int]) –

Return type:

None

train(gradient_steps, batch_size=100)[source]

Sample the replay buffer and do the updates (gradient descent and update target networks)

Return type:

None

DQN Policies

stable_baselines3.dqn.MlpPolicy

alias of DQNPolicy

class stable_baselines3.dqn.policies.DQNPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.ReLU'>, features_extractor_class=<class 'stable_baselines3.common.torch_layers.FlattenExtractor'>, features_extractor_kwargs=None, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]

Policy class with Q-Value Net and target net for DQN

Parameters:

  • observation_space (Space) – Observation space

  • action_space (Space) – Action space

  • lr_schedule (Callable[[float], float]) – Learning rate schedule (could be constant)

  • net_arch (Optional[List[int]]) – The specification of the policy and value networks.

  • activation_fn (Type[Module]) – Activation function

  • features_extractor_class (Type[BaseFeaturesExtractor]) – Features extractor to use.

  • features_extractor_kwargs (Optional[Dict[str, Any]]) – Keyword arguments to pass to the features extractor.

  • normalize_images (bool) – Whether to normalize images or not, dividing by 255.0 (True by default)

  • optimizer_class (Type[Optimizer]) – The optimizer to use, th.optim.Adam by default

  • optimizer_kwargs (Optional[Dict[str, Any]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer

forward(obs, deterministic=True)[source]

Defines the computation performed at every call.

Should be overridden by all subclasses. :rtype: Tensor

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

set_training_mode(mode)[source]

Put the policy in either training or evaluation mode.

This affects certain modules, such as batch normalisation and dropout.

Parameters:

mode (bool) – if true, set to training mode, else set to evaluation mode

Return type:

None

class stable_baselines3.dqn.CnnPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.ReLU'>, features_extractor_class=<class 'stable_baselines3.common.torch_layers.NatureCNN'>, features_extractor_kwargs=None, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]

Policy class for DQN when using images as input.

Parameters:

  • observation_space (Space) – Observation space

  • action_space (Space) – Action space

  • lr_schedule (Callable[[float], float]) – Learning rate schedule (could be constant)

  • net_arch (Optional[List[int]]) – The specification of the policy and value networks.

  • activation_fn (Type[Module]) – Activation function

  • features_extractor_class (Type[BaseFeaturesExtractor]) – Features extractor to use.

  • normalize_images (bool) – Whether to normalize images or not, dividing by 255.0 (True by default)

  • optimizer_class (Type[Optimizer]) – The optimizer to use, th.optim.Adam by default

  • optimizer_kwargs (Optional[Dict[str, Any]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer

class stable_baselines3.dqn.MultiInputPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.ReLU'>, features_extractor_class=<class 'stable_baselines3.common.torch_layers.CombinedExtractor'>, features_extractor_kwargs=None, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]

Policy class for DQN when using dict observations as input.

Parameters:

  • observation_space (Dict) – Observation space

  • action_space (Space) – Action space

  • lr_schedule (Callable[[float], float]) – Learning rate schedule (could be constant)

  • net_arch (Optional[List[int]]) – The specification of the policy and value networks.

  • activation_fn (Type[Module]) – Activation function

  • features_extractor_class (Type[BaseFeaturesExtractor]) – Features extractor to use.

  • normalize_images (bool) – Whether to normalize images or not, dividing by 255.0 (True by default)

  • optimizer_class (Type[Optimizer]) – The optimizer to use, th.optim.Adam by default

  • optimizer_kwargs (Optional[Dict[str, Any]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer