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 from 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¶
 Original paper: https://arxiv.org/abs/1707.06347
 Clear explanation of PPO on Arxiv Insights channel: https://www.youtube.com/watch?v=5P7IxPq8u8
 OpenAI blog post: https://blog.openai.com/openaibaselinesppo/
python m stable_baselines.ppo2.run_atari
runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (
h
) for more options.
python m stable_baselines.ppo2.run_mujoco
runs the algorithm for 1M frames on a Mujoco environment.
Can I use?¶
 Recurrent policies: ✔️
 Multi processing: ✔️
 Gym spaces:
Space  Action  Observation 

Discrete  ✔️  ✔️ 
Box  ✔️  ✔️ 
MultiDiscrete  ✔️  ✔️ 
MultiBinary  ✔️  ✔️ 
Example¶
Train a PPO agent on CartPolev1 using 4 processes.
import gym
from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common import make_vec_env
from stable_baselines import PPO2
# multiprocess environment
env = make_vec_env('CartPolev1', n_envs=4)
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, seed=None, n_cpu_tf_sess=None)[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 tradeoff 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
 seed – (int) Seed for the pseudorandom 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)¶ If
actions
isNone
, 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 notNone
, 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 explanationParameters:  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 logspace. 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.

get_vec_normalize_env
() → Optional[stable_baselines.common.vec_env.vec_normalize.VecNormalize]¶ Return the
VecNormalize
wrapper of the training env if it exists.Returns: Optional[VecNormalize] The VecNormalize
env.

learn
(total_timesteps, callback=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
 callback – (Union[callable, [callable], BaseCallback]) function called at every steps with state of the algorithm. It takes the local and global variables. If it returns False, training is aborted. When the callback inherits from BaseCallback, you will have access to additional stages of the training (training start/end), please read the documentation for more details.
 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 filelike) the saved parameter location
 env – (Gym Environment) 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. Ifexact_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 hyperparameters.
Warning
This function does not update trainer/optimizer variables (e.g. momentum). As such training after using this function may lead to lessthanoptimal results.
Parameters:  load_path_or_dict – (str or filelike 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=1e08, 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 filelike) The save location
 cloudpickle – (bool) Use older cloudpickle format instead of ziparchives.

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: Optional[int]) → None¶ Parameters: seed – (Optional[int]) Seed for the pseudorandom generators. If None, do not change the seeds.
Callbacks  Accessible Variables¶
Depending on initialization parameters and timestep, different variables are accessible.
Variables accessible “From timestep X” are variables that can be accessed when
self.timestep==X
in the on_step
function.
Variable Availability
 self
 total_timesteps
 callback
 log_interval
 tb_log_name
 reset_num_timesteps
 cliprange_vf
 new_tb_log
 writer
 t_first_start
 n_updates
 mb_obs
 mb_rewards
 mb_actions
 mb_values
 mb_dones
 mb_neglogpacs
 mb_states
 ep_infos
 actions
 values
 neglogpacs
 clipped_actions
 rewards
 infos
From timestep 1
 info
 maybe_ep_info
From timestep 1