PPO2

The Proximal Policy Optimization algorithm combines ideas from A2C (having multiple workers) and TRPO (it uses a trust region to improve the actor).

The main idea is that after an update, the new policy should be not too far form the old policy. For that, ppo uses clipping to avoid too large update.

Note

PPO2 is the implementation of OpenAI made for GPU. For multiprocessing, it uses vectorized environments compared to PPO1 which uses MPI.

Note

PPO2 contains several modifications from the original algorithm not documented by OpenAI: value function is also clipped and advantages are normalized.

Notes

Can I use?

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

Example

Train a PPO 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 PPO2

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

model = PPO2(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)
model.save("ppo2_cartpole")

del model # remove to demonstrate saving and loading

model = PPO2.load("ppo2_cartpole")

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

Parameters

class stable_baselines.ppo2.PPO2(policy, env, gamma=0.99, n_steps=128, ent_coef=0.01, learning_rate=0.00025, vf_coef=0.5, max_grad_norm=0.5, lam=0.95, nminibatches=4, noptepochs=4, cliprange=0.2, cliprange_vf=None, verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False)[source]

Proximal Policy Optimization algorithm (GPU version). Paper: https://arxiv.org/abs/1707.06347

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)
  • ent_coef – (float) Entropy coefficient for the loss calculation
  • learning_rate – (float or callable) The learning rate, it can be a function
  • vf_coef – (float) Value function coefficient for the loss calculation
  • max_grad_norm – (float) The maximum value for the gradient clipping
  • lam – (float) Factor for trade-off of bias vs variance for Generalized Advantage Estimator
  • nminibatches – (int) Number of training minibatches per update. For recurrent policies, the number of environments run in parallel should be a multiple of nminibatches.
  • noptepochs – (int) Number of epoch when optimizing the surrogate
  • cliprange – (float or callable) Clipping parameter, it can be a function
  • cliprange_vf – (float or callable) Clipping parameter for the value function, it can be a function. This is a parameter specific to the OpenAI implementation. If None is passed (default), then cliprange (that is used for the policy) will be used. IMPORTANT: this clipping depends on the reward scaling. To deactivate value function clipping (and recover the original PPO implementation), you have to pass a negative value (e.g. -1).
  • 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)

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()

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=1, tb_log_name='PPO2', 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
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=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