nanoPPO 0.15.post2

A flexible and efficient implementation of the Proximal Policy Optimization (PPO) algorithm for reinforcement learning.

nanoPPO

nanoPPO is a Python package that provides a simple and efficient implementation of the Proximal Policy Optimization (PPO) algorithm for reinforcement learning. It is designed to support both continuous and discrete action spaces, making it suitable for a wide range of applications.

Installation

You can install nanoPPO directly from PyPI using pip:

pip install nanoPPO

Alternatively, you can clone the repository and install from source:

git clone https://github.com/jamesliu/nanoPPO.git
cd nanoPPO
pip install .

Usage

Here are examples of how to use nanoPPO to train an agent.

On the MountaionCarContinuous-v0 environment:

from nanoppo.train_ppo_agent import train_agent
import pickle

ppo, model_file, metrics_file = train_agent(
    env_name="MountainCarContinuous-v0",
    max_episodes=50,
    policy_lr=0.0005,
    value_lr=0.0005,
    vl_coef=0.5,
    checkpoint_dir="checkpoints",
    checkpoint_interval=10,
    log_interval=10,
    wandb_log=False,
)
ppo.load(model_file)
print("Loaded best weights from", model_file)
metrics = pickle.load(open(metrics_file, "rb"))
print("Loaded metrics from", metrics_file)
best_reward = metrics["best_reward"]
episode = metrics["episode"]
print("best_reward", best_reward, "episode", episode)

Use Custom LR Scheduler and Custom Policy

• Set Cosine Annealing Learning Rate Scheduler
• Set CausalAttention Policy instead of Linear Policy

from nanoppo.train_ppo_agent import train_agent
from nanoppo.cosine_lr_scheduler import CosineLRScheduler
from nanoppo.policy.actor_critic_causal_attention import ActorCriticCausalAttention

lr_scheduler=CosineLRScheduler(
    learning_rate=config['cosine_lr'], 
    warmup_iters=config['cosine_warmup_iters'], 
    lr_decay_iters=config['cosine_decay_steps'], 
    min_lr=config['cosine_min_lr'])

policy_class = ActorCriticCausalAttention

ppo, model_file, metrics_file = train_agent(
    env_name=env_name,
    env_config = env_config, 
    max_episodes=config['max_episode'],
    stop_reward=config['stop_reward'],
    policy_class = policy_class,
    lr_scheduler=lr_scheduler,
    policy_lr=config['policy_lr'],
    value_lr=config['value_lr'],
    vl_coef=config['vl_coef'],
    betas = config['betas'],
    n_latent_var=config['n_latent_var'],
    gamma=config['gamma'],
    K_epochs=config['K_epochs'],
    eps_clip=config['eps_clip'],
    el_coef=config['el_coef'],
    checkpoint_dir=checkpoint_dir,
    checkpoint_interval=10,
    log_interval=10,
    wandb_log=wandb_log,
    debug=True)

On the CartPole-v1 environment:

from nanoppo.discrete_action_ppo import PPO
import gym

env = gym.make('CartPole-v1')
ppo = PPO(env.observation_space.shape[0], env.action_space.n)

# Training code here...

Examples

See the examples directory for more comprehensive usage examples.

examples/train_mountaincar.sh

python nanoppo/train_ppo_agent.py --env_name=MountainCarContinuous-v0 --policy_lr=0.0005 --value_lr=0.0005 --max_episodes=50 --vl_coef=0.5 --wandb_log

examples/train_pointmass1d.sh

python nanoppo/train_ppo_agent.py --env_name=PointMass1D-v0 --policy_lr=0.0005 --value_lr=0.0005 --max_episodes=50 --vl_coef=0.5 --wandb_log

examples/train_pointmass2d.sh

python nanoppo/train_ppo_agent.py --env_name=PointMass2D-v0 --policy_lr=0.0005 --value_lr=0.0005 --max_episodes=50 --vl_coef=0.5 --wandb_log

Documentation

Full documentation is available here.

Contributing

We welcome contributions to nanoPPO! If you're interested in contributing, please see our contribution guidelines.

License

nanoPPO is licensed under the Apache License 2.0. See the LICENSE file for more details.

Support

For support, questions, or feature requests, please open an issue on our GitHub repository or contact the maintainers.

Changelog

See the releases page for a detailed changelog of each version.