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.
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 = datafrom 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 = databash pip3 install plangym
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