A2C

A synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C). It uses multiple workers to avoid the use of a replay buffer.

Notes

Can I use?

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

Example

Train a A2C agent on CartPole-v1 using 4 processes.

import gym

from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import SubprocVecEnv
from stable_baselines import A2C

# multiprocess environment
n_cpu = 4
env = SubprocVecEnv([lambda: gym.make('CartPole-v1') for i in range(n_cpu)])

model = A2C(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)
model.save("a2c_cartpole")

del model # remove to demonstrate saving and loading

model = A2C.load("a2c_cartpole")

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

Parameters

class stable_baselines.a2c.A2C(policy, env, gamma=0.99, n_steps=5, vf_coef=0.25, ent_coef=0.01, max_grad_norm=0.5, learning_rate=0.0007, alpha=0.99, epsilon=1e-05, lr_schedule='constant', verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False)[source]

The A2C (Advantage Actor Critic) model class, https://arxiv.org/abs/1602.01783

Parameters:
  • policy – (ActorCriticPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, CnnLstmPolicy, …)
  • env – (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
  • gamma – (float) Discount factor
  • n_steps – (int) The number of steps to run for each environment per update (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
  • vf_coef – (float) Value function coefficient for the loss calculation
  • ent_coef – (float) Entropy coefficient for the loss caculation
  • max_grad_norm – (float) The maximum value for the gradient clipping
  • learning_rate – (float) The learning rate
  • alpha – (float) RMSProp decay parameter (default: 0.99)
  • epsilon – (float) RMSProp epsilon (stabilizes square root computation in denominator of RMSProp update) (default: 1e-5)
  • lr_schedule – (str) The type of scheduler for the learning rate update (‘linear’, ‘constant’, ‘double_linear_con’, ‘middle_drop’ or ‘double_middle_drop’)
  • 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 (used only for loading)
  • 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)

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.

Warning

When working with continuous probability distribution (e.g. Gaussian distribution for continuous action) the probability of taking a particular action is exactly zero. 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)
Returns:

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

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

Returns:(Gym Environment) The current environment
learn(total_timesteps, callback=None, seed=None, log_interval=100, tb_log_name='A2C', reset_num_timesteps=True)[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, **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)
  • kwargs – extra arguments to change the model when loading
predict(observation, state=None, mask=None, deterministic=False)

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)[source]

Save the current parameters to file

Parameters:save_path – (str or file-like object) the save location
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