training.py

from rlberry.manager import ExperimentManager
from rlberry.envs import gym_make
from rlberry.agents.stable_baselines import StableBaselinesAgent
from rlberry.seeding import Seeder

from stable_baselines3 import PPO
from avec_ppo import AVECPPO

seeder = Seeder(42)

# The ExperimentManager class is a compact way of experimenting with a deepRL agent.
default_xp = ExperimentManager(
    StableBaselinesAgent,  # The Agent class.
    (gym_make, dict(id="Acrobot-v1")),  # The Environment to solve.
    fit_budget=5e4,  # The number of interactions
    # between the agent and the
    # environment during training.
    init_kwargs=dict(algo_cls=PPO),  # Init value for StableBaselinesAgent
    eval_kwargs=dict(eval_horizon=500),  # The number of interactions
    # between the agent and the
    # environment during evaluations.
    n_fit=5,  # The number of agents to train.
    # Usually, it is good to do more
    # than 1 because the training is
    # stochastic.
    seed=seeder,
    agent_name="default_ppo",  # The agent's name.
    output_dir="data_training_default_ppo"
)

avec_xp = ExperimentManager(
    StableBaselinesAgent,  # The Agent class.
    (gym_make, dict(id="Acrobot-v1")),  # The Environment to solve.
    fit_budget=5e4,  # The number of interactions
    # between the agent and the
    # environment during training.
    init_kwargs=dict(algo_cls=AVECPPO),  # Init value for StableBaselinesAgent
    eval_kwargs=dict(eval_horizon=500),  # The number of interactions
    # between the agent and the
    # environment during evaluations.
    n_fit=5,  # The number of agents to train.
    # Usually, it is good to do more
    # than 1 because the training is
    # stochastic.
    seed=seeder,
    agent_name="avec_ppo",  # The agent's name.
    output_dir="data_training_avec_ppo"
)