Plan gym
Plangym
is an interface to use OpenAI gym for planning problems. It extends the standard
interface to allow setting and recovering the environment states.
Furthermore, it provides functionality for stepping the environments in parallel, and it is compatible with passing the parameters as vectors of steps and actions.
Getting started
Stepping a batch of states and actions
from plangym import AtariEnvironment
env = AtariEnvironment(name="MsPacman-v0",
clone_seeds=True, autoreset=True)
state, obs = env.reset()
states = [state.copy() for _ in range(10)]
actions = [env.action_space.sample() for _ in range(10)]
data = env.step_batch(states=states, actions=actions)
new_states, observs, rewards, ends, infos = data
Using parallel steps
from plangym import AtariEnvironment, ParallelEnvironment
env = ParallelEnvironment(env_class=AtariEnvironment,
name="MsPacman-v0",
clone_seeds=True, autoreset=True,
blocking=False)
state, obs = env.reset()
states = [state.copy() for _ in range(10)]
actions = [env.action_space.sample() for _ in range(10)]
data = env.step_batch(states=states,
actions=actions)
new_states, observs, rewards, ends, infos = data
Installation
bash pip3 install plangym