QR-DQN¶
Quantile Regression DQN (QR-DQN) builds on Deep Q-Network (DQN) and make use of quantile regression to explicitly model the distribution over returns, instead of predicting the mean return (DQN).
Available Policies
alias of |
|
Policy class for QR-DQN when using images as input. |
Notes¶
Original paper: https://arxiv.org/abs/1710.100442
Distributional RL (C51): https://arxiv.org/abs/1707.06887
Further reference: https://github.com/amy12xx/ml_notes_and_reports/blob/master/distributional_rl/QRDQN.pdf
Can I use?¶
Recurrent policies: ❌
Multi processing: ❌
Gym spaces:
Space |
Action |
Observation |
---|---|---|
Discrete |
✔ |
✔ |
Box |
❌ |
✔ |
MultiDiscrete |
❌ |
✔ |
MultiBinary |
❌ |
✔ |
Example¶
import gym
from sb3_contrib import QRDQN
env = gym.make("CartPole-v1")
policy_kwargs = dict(n_quantiles=50)
model = QRDQN("MlpPolicy", env, policy_kwargs=policy_kwargs, verbose=1)
model.learn(total_timesteps=10000, log_interval=4)
model.save("qrdqn_cartpole")
del model # remove to demonstrate saving and loading
model = QRDQN.load("qrdqn_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¶
Result on Atari environments (10M steps, Pong and Breakout) and classic control tasks using 3 and 5 seeds.
The complete learning curves are available in the associated PR.
Note
QR-DQN implementation was validated against Intel Coach one which roughly compare to the original paper results (we trained the agent with a smaller budget).
Environments |
QR-DQN |
DQN |
---|---|---|
Breakout |
413 +/- 21 |
~300 |
Pong |
20 +/- 0 |
~20 |
CartPole |
386 +/- 64 |
500 +/- 0 |
MountainCar |
-111 +/- 4 |
-107 +/- 4 |
LunarLander |
168 +/- 39 |
195 +/- 28 |
Acrobot |
-73 +/- 2 |
-74 +/- 2 |
How to replicate the results?¶
Clone RL-Zoo fork and checkout the branch feat/qrdqn
:
git clone https://github.com/ku2482/rl-baselines3-zoo/
cd rl-baselines3-zoo/
git checkout feat/qrdqn
Run the benchmark (replace $ENV_ID
by the envs mentioned above):
python train.py --algo qrdqn --env $ENV_ID --eval-episodes 10 --eval-freq 10000
Plot the results:
python scripts/all_plots.py -a qrdqn -e Breakout Pong -f logs/ -o logs/qrdqn_results
python scripts/plot_from_file.py -i logs/qrdqn_results.pkl -latex -l QR-DQN
Parameters¶
- class sb3_contrib.qrdqn.QRDQN(policy, env, learning_rate=5e-05, buffer_size=1000000, learning_starts=50000, batch_size=32, tau=1.0, gamma=0.99, train_freq=4, gradient_steps=1, optimize_memory_usage=False, target_update_interval=10000, exploration_fraction=0.005, exploration_initial_eps=1.0, exploration_final_eps=0.01, max_grad_norm=None, tensorboard_log=None, create_eval_env=False, policy_kwargs=None, verbose=0, seed=None, device='auto', _init_setup_model=True)[source]¶
Quantile Regression Deep Q-Network (QR-DQN) Paper: https://arxiv.org/abs/1710.10044 Default hyperparameters are taken from the paper and are tuned for Atari games.
- Parameters
policy (
Union
[str
,Type
[QRDQNPolicy
]]) – 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 bufferlearning_starts (
int
) – how many steps of the model to collect transitions for before learning startsbatch_size (
Optional
[int
]) – Minibatch size for each gradient updatetau (
float
) – the soft update coefficient (“Polyak update”, between 0 and 1) default 1 for hard updategamma (
float
) – the discount factortrain_freq (
int
) – Update the model everytrain_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 (seetrain_freq
andn_episodes_rollout
) Set to-1
means to do as many gradient steps as steps done in the environment during the rollout.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-637501195target_update_interval (
int
) – update the target network everytarget_update_interval
environment steps.exploration_fraction (
float
) – fraction of entire training period over which the exploration rate is reducedexploration_initial_eps (
float
) – initial value of random action probabilityexploration_final_eps (
float
) – final value of random action probabilitymax_grad_norm (
Optional
[float
]) – The maximum value for the gradient clipping (if None, no clipping)tensorboard_log (
Optional
[str
]) – the log location for tensorboard (if None, no logging)create_eval_env (
bool
) – Whether to create a second environment that will be used for evaluating the agent periodically. (Only available when passing string for the environment)policy_kwargs (
Optional
[Dict
[str
,Any
]]) – additional arguments to be passed to the policy on creationverbose (
int
) – the verbosity level: 0 no output, 1 info, 2 debugseed (
Optional
[int
]) – Seed for the pseudo random generatorsdevice (
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 environmentcallback (
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. EitherTrainFreq(<n>, TrainFrequencyUnit.STEP)
orTrainFreq(<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 everylog_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, eval_env=None, eval_freq=- 1, n_eval_episodes=5, tb_log_name='QRDQN', eval_log_path=None, reset_num_timesteps=True)[source]¶
Return a trained model.
- Parameters
total_timesteps (
int
) – The total number of samples (env steps) to train oncallback (
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 loggingeval_env (
Union
[Env
,VecEnv
,None
]) – Environment that will be used to evaluate the agenteval_freq (
int
) – Evaluate the agent everyeval_freq
timesteps (this may vary a little)n_eval_episodes (
int
) – Number of episode to evaluate the agenteval_log_path (
Optional
[str
]) – Path to a folder where the evaluations will be savedreset_num_timesteps (
bool
) – whether or not to reset the current timestep number (used in logging)
- Return type
OffPolicyAlgorithm
- 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
- Parameters
path (
Union
[str
,Path
,BufferedIOBase
]) – path to the file (or a file-like) where to load the agent fromenv (
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 environmentdevice (
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 inkeras.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 toreset()
before training to avoid unexpected behavior. See https://github.com/DLR-RM/stable-baselines3/issues/597kwargs – extra arguments to change the model when loading
- Return type
BaseAlgorithm
- 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 usingHerReplayBuffer
with online sampling: If set toTrue
, we assume that the last trajectory in the replay buffer was finished (and truncate it). If set toFalse
, we assume that we continue the same trajectory (same episode).
- Return type
None
- property logger: stable_baselines3.common.logger.Logger¶
Getter for the logger object.
- Return type
Logger
- predict(observation, state=None, mask=None, deterministic=False)[source]¶
Overrides the base_class predict function to include epsilon-greedy exploration.
- Parameters
observation (
ndarray
) – the input observationstate (
Optional
[ndarray
]) – The last states (can be None, used in recurrent policies)mask (
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
[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 savedexclude (
Optional
[Iterable
[str
]]) – name of parameters that should be excluded in addition to the default onesinclude (
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
env (
Union
[Env
,VecEnv
]) – The environment for learning a policyforce_reset (
bool
) – Force call toreset()
before training to avoid unexpected behavior. See issue https://github.com/DLR-RM/stable-baselines3/issues/597
- Return type
None
- set_logger(logger)¶
Setter for for logger object.
Warning
When passing a custom logger object, this will overwrite
tensorboard_log
andverbose
settings passed to the constructor.- Return type
None
- 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 bytorch.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
QR-DQN Policies¶
- sb3_contrib.qrdqn.MlpPolicy¶
alias of
sb3_contrib.qrdqn.policies.QRDQNPolicy
- class sb3_contrib.qrdqn.policies.QRDQNPolicy(observation_space, action_space, lr_schedule, n_quantiles=200, 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 quantile and target networks for QR-DQN.
- Parameters
observation_space (
Space
) – Observation spaceaction_space (
Space
) – Action spacelr_schedule (
Callable
[[float
],float
]) – Learning rate schedule (could be constant)n_quantiles (
int
) – Number of quantilesnet_arch (
Optional
[List
[int
]]) – The specification of the network architecture.activation_fn (
Type
[Module
]) – Activation functionfeatures_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 defaultoptimizer_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.
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.- Return type
Tensor
- class sb3_contrib.qrdqn.CnnPolicy(observation_space, action_space, lr_schedule, n_quantiles=200, 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 QR-DQN when using images as input.
- Parameters
observation_space (
Space
) – Observation spaceaction_space (
Space
) – Action spacelr_schedule (
Callable
[[float
],float
]) – Learning rate schedule (could be constant)n_quantiles (
int
) – Number of quantilesnet_arch (
Optional
[List
[int
]]) – The specification of the network architecture.activation_fn (
Type
[Module
]) – Activation functionfeatures_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 defaultoptimizer_kwargs (
Optional
[Dict
[str
,Any
]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer