rllite

This is a lite repository for reinforcement learning using PyTorch

How to install

Requirements

• Python >= 3.5
• Pytorch >= 0.4

Install Stable Version

pip install rllite

Install Latest Version

git clone https://github.com/ZJU-RL/rllite.git
cd rllite
python setup.py install

How to use

Simple

from rllite import SAC
model = SAC('Pendulum-v0').learn(1e6)

Normal

from rllite import SAC
# set
model = SAC(
    env_name = 'Pendulum-v0',
    load_dir = './ckpt',
    log_dir = "./log",
    buffer_size = 1e6,
    seed = 1,
    max_episode_steps = None,
    batch_size = 64,
    discount = 0.99,
    learning_starts = 500,
    tau = 0.005,
    save_eps_num = 100
	)
# train
model.learn(1e6)
# eval
for _ in range(10):
    done = False
    obs = model.env.reset()
    while not done:
        action = model.predict(obs)
        obs, reward, done, info = model.env.step(action)
        model.env.render()
model.env.close()

Complex

import gym
from rllite import SAC
from rllite.common import choose_gpu, GymDelay
# choose your GPU if you have more than one
choose_gpu(0)

# your own gym environment
env = gym.make("Pendulum-v0")
env = GymDelay(env, 2, 2)
    
# set
model = SAC(
    external_env = env, # import your env
    env_name = "my_env", # your env name
    load_dir = './ckpt',
    log_dir = "./log",
    buffer_size = 1e6,
    seed = 1,
    max_episode_steps = 500, # manual set
    batch_size = 64,
    discount = 0.99,
    learning_starts = 1000,
    tau = 0.005,
    save_eps_num = 100
	)

timesteps = 0
total_timesteps = 1e6
max_eps_steps = 500

# train
while timesteps < total_timesteps:
    done = False
    eps_steps = 0
    obs = model.env.reset()
    while not done and eps_steps < max_eps_steps:
        action = model.predict(obs)
        new_obs, reward, done, info = model.env.step(action)
        model.replay_buffer.push(obs, action, reward, new_obs, done)
        obs = new_obs
        eps_steps += 1
        timesteps += 1
        if timesteps > model.learning_starts :
            model.train_step()
            
# eval
for _ in range(10):
    done = False
    obs = model.env.reset()
    while not done:
        action = model.predict(obs)
        obs, reward, done, info = model.env.step(action)
        model.env.render()
model.env.close()

Citing

If you use rllite in your research, you can cite it as follows:

@misc{wang2019rllite,
    author = {Yunkai Wang and
    	      Haodong Zhang},
    title = {rllite},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/ZJU-RL/rllite}},
}