TRPO¶
Trust Region Policy Optimization (TRPO) is an iterative approach for optimizing policies with guaranteed monotonic improvement.
Notes¶
- Original paper: https://arxiv.org/abs/1502.05477
- OpenAI blog post: https://blog.openai.com/openai-baselines-ppo/
mpirun -np 16 python -m stable_baselines.trpo_mpi.run_atari
runs the algorithm for 40M frames = 10M timesteps on an Atari game. See help (-h
) for more options.python -m stable_baselines.trpo_mpi.run_mujoco
runs the algorithm for 1M timesteps on a Mujoco environment.
Can I use?¶
- Recurrent policies: ✔️
- Multi processing: ✔️ (using MPI)
- Gym spaces:
Space | Action | Observation |
---|---|---|
Discrete | ✔️ | ✔️ |
Box | ✔️ | ✔️ |
MultiDiscrete | ✔️ | ✔️ |
MultiBinary | ✔️ | ✔️ |
Example¶
import gym
from stable_baselines.common.policies import MlpPolicy, MlpLstmPolicy, MlpLnLstmPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import TRPO
env = gym.make('CartPole-v1')
env = DummyVecEnv([lambda: env])
model = TRPO(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=25000)
model.save("trpo_cartpole")
del model # remove to demonstrate saving and loading
model = TRPO.load("trpo_cartpole")
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
Parameters¶
-
class
stable_baselines.trpo_mpi.
TRPO
(policy, env, gamma=0.99, timesteps_per_batch=1024, max_kl=0.01, cg_iters=10, lam=0.98, entcoeff=0.0, cg_damping=0.01, vf_stepsize=0.0003, vf_iters=3, verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None)[source]¶ -
action_probability
(observation, state=None, mask=None, actions=None)¶ 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.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='TRPO')[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
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)
-
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
-