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?

  • Reccurent 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, verbose=0, tensorboard_log=None, _init_setup_model=True)[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 caculation
  • 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
  • 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
action_probability(observation, state=None, mask=None)

Get the model’s action probability distribution 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)
Returns:

(np.ndarray) the model’s action probability distribution
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=1, tb_log_name='PPO2')[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)) function called at every steps with state of the algorithm. It takes the local and global variables.
  • log_interval – (int) The number of timesteps before logging.
  • tb_log_name – (str) the name of the run for tensorboard log
Returns:

(BaseRLModel) the trained model
classmethod load(load_path, env=None, **kwargs)

Load the model from file

Parameters:
  • load_path – (str) 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)
save(save_path)[source]

Save the current parameters to file

Parameters:save_path – (str) 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