PPO¶
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
PPO contains several modifications from the original algorithm not documented by OpenAI: advantages are normalized and value function can be also clipped .
Notes¶
Original paper: https://arxiv.org/abs/1707.06347
Clear explanation of PPO on Arxiv Insights channel: https://www.youtube.com/watch?v=5P7I-xPq8u8
OpenAI blog post: https://blog.openai.com/openai-baselines-ppo/
Spinning Up guide: https://spinningup.openai.com/en/latest/algorithms/ppo.html
Can I use?¶
Recurrent policies: ❌
Multi processing: ✔️
Gym spaces:
Space |
Action |
Observation |
---|---|---|
Discrete |
✔️ |
✔️ |
Box |
✔️ |
✔️ |
MultiDiscrete |
✔️ |
✔️ |
MultiBinary |
✔️ |
✔️ |
Dict |
❌ |
✔️ |
Example¶
This example is only to demonstrate the use of the library and its functions, and the trained agents may not solve the environments. Optimized hyperparameters can be found in RL Zoo repository.
Train a PPO agent on CartPole-v1
using 4 environments.
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
# Parallel environments
env = make_vec_env("CartPole-v1", n_envs=4)
model = PPO("MlpPolicy", env, verbose=1)
model.learn(total_timesteps=25000)
model.save("ppo_cartpole")
del model # remove to demonstrate saving and loading
model = PPO.load("ppo_cartpole")
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
Results¶
Atari Games¶
The complete learning curves are available in the associated PR #110.
PyBullet Environments¶
Results on the PyBullet benchmark (2M steps) using 6 seeds. The complete learning curves are available in the associated issue #48.
Note
Hyperparameters from the gSDE paper were used (as they are tuned for PyBullet envs).
Gaussian means that the unstructured Gaussian noise is used for exploration, gSDE (generalized State-Dependent Exploration) is used otherwise.
Environments |
A2C |
A2C |
PPO |
PPO |
---|---|---|---|---|
Gaussian |
gSDE |
Gaussian |
gSDE |
|
HalfCheetah |
2003 +/- 54 |
2032 +/- 122 |
1976 +/- 479 |
2826 +/- 45 |
Ant |
2286 +/- 72 |
2443 +/- 89 |
2364 +/- 120 |
2782 +/- 76 |
Hopper |
1627 +/- 158 |
1561 +/- 220 |
1567 +/- 339 |
2512 +/- 21 |
Walker2D |
577 +/- 65 |
839 +/- 56 |
1230 +/- 147 |
2019 +/- 64 |
How to replicate the results?¶
Clone the rl-zoo repo:
git clone https://github.com/DLR-RM/rl-baselines3-zoo
cd rl-baselines3-zoo/
Run the benchmark (replace $ENV_ID
by the envs mentioned above):
python train.py --algo ppo --env $ENV_ID --eval-episodes 10 --eval-freq 10000
Plot the results (here for PyBullet envs only):
python scripts/all_plots.py -a ppo -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/ppo_results
python scripts/plot_from_file.py -i logs/ppo_results.pkl -latex -l PPO
Parameters¶
- class stable_baselines3.ppo.PPO(policy, env, learning_rate=0.0003, n_steps=2048, batch_size=64, n_epochs=10, gamma=0.99, gae_lambda=0.95, clip_range=0.2, clip_range_vf=None, ent_coef=0.0, vf_coef=0.5, max_grad_norm=0.5, use_sde=False, sde_sample_freq=-1, target_kl=None, tensorboard_log=None, create_eval_env=False, policy_kwargs=None, verbose=0, seed=None, device='auto', _init_setup_model=True)[source]¶
Proximal Policy Optimization algorithm (PPO) (clip version)
Paper: https://arxiv.org/abs/1707.06347 Code: This implementation borrows code from OpenAI Spinning Up (https://github.com/openai/spinningup/) https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and and Stable Baselines (PPO2 from https://github.com/hill-a/stable-baselines)
Introduction to PPO: https://spinningup.openai.com/en/latest/algorithms/ppo.html
- Parameters:
policy (
Union
[str
,Type
[ActorCriticPolicy
]]) – The policy model to use (MlpPolicy, CnnPolicy, …)env (
Union
[Env
,VecEnv
,str
]) – The environment to learn from (if registered in Gym, can be str)learning_rate (
Union
[float
,Callable
[[float
],float
]]) – The learning rate, it can be a function of the current progress remaining (from 1 to 0)n_steps (
int
) – The number of steps to run for each environment per update (i.e. rollout buffer size is n_steps * n_envs where n_envs is number of environment copies running in parallel) NOTE: n_steps * n_envs must be greater than 1 (because of the advantage normalization) See https://github.com/pytorch/pytorch/issues/29372batch_size (
int
) – Minibatch sizen_epochs (
int
) – Number of epoch when optimizing the surrogate lossgamma (
float
) – Discount factorgae_lambda (
float
) – Factor for trade-off of bias vs variance for Generalized Advantage Estimatorclip_range (
Union
[float
,Callable
[[float
],float
]]) – Clipping parameter, it can be a function of the current progress remaining (from 1 to 0).clip_range_vf (
Union
[None
,float
,Callable
[[float
],float
]]) – Clipping parameter for the value function, it can be a function of the current progress remaining (from 1 to 0). This is a parameter specific to the OpenAI implementation. If None is passed (default), no clipping will be done on the value function. IMPORTANT: this clipping depends on the reward scaling.ent_coef (
float
) – Entropy coefficient for the loss calculationvf_coef (
float
) – Value function coefficient for the loss calculationmax_grad_norm (
float
) – The maximum value for the gradient clippinguse_sde (
bool
) – Whether to use generalized State Dependent Exploration (gSDE) instead of action noise exploration (default: False)sde_sample_freq (
int
) – Sample a new noise matrix every n steps when using gSDE Default: -1 (only sample at the beginning of the rollout)target_kl (
Optional
[float
]) – Limit the KL divergence between updates, because the clipping is not enough to prevent large update see issue #213 (cf https://github.com/hill-a/stable-baselines/issues/213) By default, there is no limit on the kl div.tensorboard_log (
Optional
[str
]) – the log location for tensorboard (if None, no logging)create_eval_env (
bool
) – Whether to create a second environment that will be used for evaluating the agent periodically. (Only available when passing string for the environment)policy_kwargs (
Optional
[Dict
[str
,Any
]]) – additional arguments to be passed to the policy on creationverbose (
int
) – the verbosity level: 0 no output, 1 info, 2 debugseed (
Optional
[int
]) – Seed for the pseudo random generatorsdevice (
Union
[device
,str
]) – Device (cpu, cuda, …) on which the code should be run. Setting it to auto, the code will be run on the GPU if possible._init_setup_model (
bool
) – Whether or not to build the network at the creation of the instance
- collect_rollouts(env, callback, rollout_buffer, n_rollout_steps)¶
Collect experiences using the current policy and fill a
RolloutBuffer
. The term rollout here refers to the model-free notion and should not be used with the concept of rollout used in model-based RL or planning.- Parameters:
env (
VecEnv
) – The training environmentcallback (
BaseCallback
) – Callback that will be called at each step (and at the beginning and end of the rollout)rollout_buffer (
RolloutBuffer
) – Buffer to fill with rolloutsn_steps – Number of experiences to collect per environment
- Return type:
bool
- Returns:
True if function returned with at least n_rollout_steps collected, False if callback terminated rollout prematurely.
- get_env()¶
Returns the current environment (can be None if not defined).
- Return type:
Optional
[VecEnv
]- Returns:
The current environment
- get_parameters()¶
Return the parameters of the agent. This includes parameters from different networks, e.g. critics (value functions) and policies (pi functions).
- Return type:
Dict
[str
,Dict
]- Returns:
Mapping of from names of the objects to PyTorch state-dicts.
- get_vec_normalize_env()¶
Return the
VecNormalize
wrapper of the training env if it exists.- Return type:
Optional
[VecNormalize
]- Returns:
The
VecNormalize
env.
- learn(total_timesteps, callback=None, log_interval=1, eval_env=None, eval_freq=-1, n_eval_episodes=5, tb_log_name='PPO', eval_log_path=None, reset_num_timesteps=True)[source]¶
Return a trained model.
- Parameters:
total_timesteps (
int
) – The total number of samples (env steps) to train oncallback (
Union
[None
,Callable
,List
[BaseCallback
],BaseCallback
]) – callback(s) called at every step with state of the algorithm.log_interval (
int
) – The number of timesteps before logging.tb_log_name (
str
) – the name of the run for TensorBoard loggingeval_env (
Union
[Env
,VecEnv
,None
]) – Environment that will be used to evaluate the agenteval_freq (
int
) – Evaluate the agent everyeval_freq
timesteps (this may vary a little)n_eval_episodes (
int
) – Number of episode to evaluate the agenteval_log_path (
Optional
[str
]) – Path to a folder where the evaluations will be savedreset_num_timesteps (
bool
) – whether or not to reset the current timestep number (used in logging)
- Return type:
- Returns:
the trained model
- classmethod load(path, env=None, device='auto', custom_objects=None, print_system_info=False, force_reset=True, **kwargs)¶
Load the model from a zip-file. Warning:
load
re-creates the model from scratch, it does not update it in-place! For an in-place load useset_parameters
instead.- Parameters:
path (
Union
[str
,Path
,BufferedIOBase
]) – path to the file (or a file-like) where to load the agent fromenv (
Union
[Env
,VecEnv
,None
]) – the new environment to run the loaded model on (can be None if you only need prediction from a trained model) has priority over any saved environmentdevice (
Union
[device
,str
]) – Device on which the code should run.custom_objects (
Optional
[Dict
[str
,Any
]]) – 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 inkeras.models.load_model
. Useful when you have an object in file that can not be deserialized.print_system_info (
bool
) – Whether to print system info from the saved model and the current system info (useful to debug loading issues)force_reset (
bool
) – Force call toreset()
before training to avoid unexpected behavior. See https://github.com/DLR-RM/stable-baselines3/issues/597kwargs – extra arguments to change the model when loading
- Return type:
- Returns:
new model instance with loaded parameters
- predict(observation, state=None, episode_start=None, deterministic=False)¶
Get the policy action from an observation (and optional hidden state). Includes sugar-coating to handle different observations (e.g. normalizing images).
- Parameters:
observation (
ndarray
) – the input observationstate (
Optional
[Tuple
[ndarray
,...
]]) – The last hidden states (can be None, used in recurrent policies)episode_start (
Optional
[ndarray
]) – The last masks (can be None, used in recurrent policies) this correspond to beginning of episodes, where the hidden states of the RNN must be reset.deterministic (
bool
) – Whether or not to return deterministic actions.
- Return type:
Tuple
[ndarray
,Optional
[Tuple
[ndarray
,...
]]]- Returns:
the model’s action and the next hidden state (used in recurrent policies)
- save(path, exclude=None, include=None)¶
Save all the attributes of the object and the model parameters in a zip-file.
- Parameters:
path (
Union
[str
,Path
,BufferedIOBase
]) – path to the file where the rl agent should be savedexclude (
Optional
[Iterable
[str
]]) – name of parameters that should be excluded in addition to the default onesinclude (
Optional
[Iterable
[str
]]) – name of parameters that might be excluded but should be included anyway
- Return type:
None
- set_env(env, force_reset=True)¶
Checks the validity of the environment, and if it is coherent, set it as the current environment. Furthermore wrap any non vectorized env into a vectorized checked parameters: - observation_space - action_space
- Parameters:
env (
Union
[Env
,VecEnv
]) – The environment for learning a policyforce_reset (
bool
) – Force call toreset()
before training to avoid unexpected behavior. See issue https://github.com/DLR-RM/stable-baselines3/issues/597
- Return type:
None
- set_logger(logger)¶
Setter for for logger object. :rtype:
None
Warning
When passing a custom logger object, this will overwrite
tensorboard_log
andverbose
settings passed to the constructor.
- set_parameters(load_path_or_dict, exact_match=True, device='auto')¶
Load parameters from a given zip-file or a nested dictionary containing parameters for different modules (see
get_parameters
).- Parameters:
load_path_or_iter – Location of the saved data (path or file-like, see
save
), or a nested dictionary containing nn.Module parameters used by the policy. The dictionary maps object names to a state-dictionary returned bytorch.nn.Module.state_dict()
.exact_match (
bool
) – If True, the given parameters should include parameters for each module and each of their parameters, otherwise raises an Exception. If set to False, this can be used to update only specific parameters.device (
Union
[device
,str
]) – Device on which the code should run.
- Return type:
None
- set_random_seed(seed=None)¶
Set the seed of the pseudo-random generators (python, numpy, pytorch, gym, action_space)
- Parameters:
seed (
Optional
[int
]) –- Return type:
None
PPO Policies¶
- stable_baselines3.ppo.MlpPolicy¶
alias of
ActorCriticPolicy
- class stable_baselines3.common.policies.ActorCriticPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.Tanh'>, ortho_init=True, use_sde=False, log_std_init=0.0, full_std=True, sde_net_arch=None, use_expln=False, squash_output=False, features_extractor_class=<class 'stable_baselines3.common.torch_layers.FlattenExtractor'>, features_extractor_kwargs=None, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]
Policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes.
- Parameters:
observation_space (
Space
) – Observation spaceaction_space (
Space
) – Action spacelr_schedule (
Callable
[[float
],float
]) – Learning rate schedule (could be constant)net_arch (
Optional
[List
[Union
[int
,Dict
[str
,List
[int
]]]]]) – The specification of the policy and value networks.activation_fn (
Type
[Module
]) – Activation functionortho_init (
bool
) – Whether to use or not orthogonal initializationuse_sde (
bool
) – Whether to use State Dependent Exploration or notlog_std_init (
float
) – Initial value for the log standard deviationfull_std (
bool
) – Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDEsde_net_arch (
Optional
[List
[int
]]) – Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features.use_expln (
bool
) – Useexpln()
function instead ofexp()
to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice,exp()
is usually enough.squash_output (
bool
) – Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE.features_extractor_class (
Type
[BaseFeaturesExtractor
]) – Features extractor to use.features_extractor_kwargs (
Optional
[Dict
[str
,Any
]]) – Keyword arguments to pass to the features extractor.normalize_images (
bool
) – Whether to normalize images or not, dividing by 255.0 (True by default)optimizer_class (
Type
[Optimizer
]) – The optimizer to use,th.optim.Adam
by defaultoptimizer_kwargs (
Optional
[Dict
[str
,Any
]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer
- evaluate_actions(obs, actions)[source]
Evaluate actions according to the current policy, given the observations.
- Parameters:
obs (
Tensor
) –actions (
Tensor
) –
- Return type:
Tuple
[Tensor
,Tensor
,Tensor
]- Returns:
estimated value, log likelihood of taking those actions and entropy of the action distribution.
- forward(obs, deterministic=False)[source]
Forward pass in all the networks (actor and critic)
- Parameters:
obs (
Tensor
) – Observationdeterministic (
bool
) – Whether to sample or use deterministic actions
- Return type:
Tuple
[Tensor
,Tensor
,Tensor
]- Returns:
action, value and log probability of the action
- get_distribution(obs)[source]
Get the current policy distribution given the observations.
- Parameters:
obs (
Tensor
) –- Return type:
- Returns:
the action distribution.
- predict_values(obs)[source]
Get the estimated values according to the current policy given the observations.
- Parameters:
obs (
Tensor
) –- Return type:
Tensor
- Returns:
the estimated values.
- reset_noise(n_envs=1)[source]
Sample new weights for the exploration matrix.
- Parameters:
n_envs (
int
) –- Return type:
None
- stable_baselines3.ppo.CnnPolicy¶
alias of
ActorCriticCnnPolicy
- class stable_baselines3.common.policies.ActorCriticCnnPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.Tanh'>, ortho_init=True, use_sde=False, log_std_init=0.0, full_std=True, sde_net_arch=None, use_expln=False, squash_output=False, features_extractor_class=<class 'stable_baselines3.common.torch_layers.NatureCNN'>, features_extractor_kwargs=None, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]
CNN policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes.
- Parameters:
observation_space (
Space
) – Observation spaceaction_space (
Space
) – Action spacelr_schedule (
Callable
[[float
],float
]) – Learning rate schedule (could be constant)net_arch (
Optional
[List
[Union
[int
,Dict
[str
,List
[int
]]]]]) – The specification of the policy and value networks.activation_fn (
Type
[Module
]) – Activation functionortho_init (
bool
) – Whether to use or not orthogonal initializationuse_sde (
bool
) – Whether to use State Dependent Exploration or notlog_std_init (
float
) – Initial value for the log standard deviationfull_std (
bool
) – Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDEsde_net_arch (
Optional
[List
[int
]]) – Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features.use_expln (
bool
) – Useexpln()
function instead ofexp()
to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice,exp()
is usually enough.squash_output (
bool
) – Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE.features_extractor_class (
Type
[BaseFeaturesExtractor
]) – Features extractor to use.features_extractor_kwargs (
Optional
[Dict
[str
,Any
]]) – Keyword arguments to pass to the features extractor.normalize_images (
bool
) – Whether to normalize images or not, dividing by 255.0 (True by default)optimizer_class (
Type
[Optimizer
]) – The optimizer to use,th.optim.Adam
by defaultoptimizer_kwargs (
Optional
[Dict
[str
,Any
]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer
- stable_baselines3.ppo.MultiInputPolicy¶
alias of
MultiInputActorCriticPolicy
- class stable_baselines3.common.policies.MultiInputActorCriticPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.Tanh'>, ortho_init=True, use_sde=False, log_std_init=0.0, full_std=True, sde_net_arch=None, use_expln=False, squash_output=False, features_extractor_class=<class 'stable_baselines3.common.torch_layers.CombinedExtractor'>, features_extractor_kwargs=None, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]
MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes.
- Parameters:
observation_space (
Dict
) – Observation space (Tuple)action_space (
Space
) – Action spacelr_schedule (
Callable
[[float
],float
]) – Learning rate schedule (could be constant)net_arch (
Optional
[List
[Union
[int
,Dict
[str
,List
[int
]]]]]) – The specification of the policy and value networks.activation_fn (
Type
[Module
]) – Activation functionortho_init (
bool
) – Whether to use or not orthogonal initializationuse_sde (
bool
) – Whether to use State Dependent Exploration or notlog_std_init (
float
) – Initial value for the log standard deviationfull_std (
bool
) – Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDEsde_net_arch (
Optional
[List
[int
]]) – Network architecture for extracting features when using gSDE. If None, the latent features from the policy will be used. Pass an empty list to use the states as features.use_expln (
bool
) – Useexpln()
function instead ofexp()
to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice,exp()
is usually enough.squash_output (
bool
) – Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE.features_extractor_class (
Type
[BaseFeaturesExtractor
]) – Uses the CombinedExtractorfeatures_extractor_kwargs (
Optional
[Dict
[str
,Any
]]) – Keyword arguments to pass to the feature extractor.normalize_images (
bool
) – Whether to normalize images or not, dividing by 255.0 (True by default)optimizer_class (
Type
[Optimizer
]) – The optimizer to use,th.optim.Adam
by defaultoptimizer_kwargs (
Optional
[Dict
[str
,Any
]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer