Local Storage

API Overview

Minari’s API easily lets you save, load, list and delete locally stored datasets.

        truncations=replay_buffer["truncated"],
    )

    print("Dataset generated!")

    return ds


if __name__ == "__main__":
    dataset_name = "LunarLander_v2_test-dataset"

    print("\n Generate dataset as standard")
    generated_dataset = generate_dataset(dataset_name)

    print("\n Save dataset to local storage")
    generated_dataset.save()

    print(
        "\n Listing datasets in local storage, we should see the dataset we just generated"
    )
    minari.list_local_datasets()

    print("\n We can load the dataset from local storage as follows")

Complete Working Example

Environment Setup

To run the full code below, you will need to install the dependencies shown below. It is recommended to use a newly-created virtual environment to avoid dependency conflicts.

#gymnasium[box2d]  # todo: vc
cython
#minari
h5py
structlog
tensorboardX
typing_extensions
git+https://github.com/WillDudley/Gymnasium.git@spec_stack#egg=gymnasium[box2d]

Then you need to pip install -e . from the root of the repository.

Full Code

# pyright: basic, reportOptionalMemberAccess=false, reportOptionalSubscript=false
import json

import gymnasium as gym
import numpy as np
from gymnasium.utils.serialize_spec_stack import serialise_spec_stack

import minari
from minari.dataset import MinariDataset


def generate_dataset(dataset_name: str):
    num_episodes = 10

    env = gym.make("LunarLander-v2", render_mode="rgb_array")
    environment_stack = serialise_spec_stack(env.spec_stack)
    observation, info = env.reset(seed=42)

    assert env.spec.max_episode_steps is not None, "Max episode steps must be defined"

    replay_buffer = {
        "episode": np.array(
            [[0]] * env.spec.max_episode_steps * num_episodes, dtype=np.int32
        ),
        "observation": np.array(
            [[0] * env.observation_space.shape[0]]
            * env.spec.max_episode_steps
            * num_episodes,
            dtype=np.float32,
        ),
        "action": np.array(
            [[0] * 2] * env.spec.max_episode_steps * num_episodes, dtype=np.float32
        ),
        "reward": np.array(
            [[0]] * env.spec.max_episode_steps * num_episodes, dtype=np.float32
        ),
        "terminated": np.array(
            [[0]] * env.spec.max_episode_steps * num_episodes, dtype=bool
        ),
        "truncated": np.array(
            [[0]] * env.spec.max_episode_steps * num_episodes, dtype=bool
        ),
    }

    total_steps = 0
    for episode in range(num_episodes):
        episode_step = 0
        observation, info = env.reset()
        terminated = False
        truncated = False
        while not terminated and not truncated:
            action = env.action_space.sample()  # User-defined policy function
            observation, reward, terminated, truncated, info = env.step(action)
            replay_buffer["episode"][total_steps] = np.array(episode)
            replay_buffer["observation"][total_steps] = np.array(observation)
            replay_buffer["action"][total_steps] = np.array(action)
            replay_buffer["reward"][total_steps] = np.array(reward)
            replay_buffer["terminated"][total_steps] = np.array(terminated)
            replay_buffer["truncated"][total_steps] = np.array(truncated)
            episode_step += 1
            total_steps += 1

    env.close()

    replay_buffer["episode"] = replay_buffer["episode"][:total_steps]
    replay_buffer["observation"] = replay_buffer["observation"][:total_steps]
    replay_buffer["action"] = replay_buffer["action"][:total_steps]
    replay_buffer["reward"] = replay_buffer["reward"][:total_steps]
    replay_buffer["terminated"] = replay_buffer["terminated"][:total_steps]
    replay_buffer["truncated"] = replay_buffer["truncated"][:total_steps]

    ds = MinariDataset(
        dataset_name=dataset_name,
        algorithm_name="random_policy",
        environment_name="LunarLander-v2",
        environment_stack=json.dumps(environment_stack),
        seed_used=42,  # For the simplicity of this example, we're not actually using a seed. Naughty us!
        code_permalink="https://github.com/Farama-Foundation/Minari/blob/f095bfe07f8dc6642082599e07779ec1dd9b2667/tutorials/LocalStorage/local_storage.py",
        author="WillDudley",
        author_email="wdudley@farama.org",
        observations=replay_buffer["observation"],
        actions=replay_buffer["action"],
        rewards=replay_buffer["reward"],
        terminations=replay_buffer["terminated"],
        truncations=replay_buffer["truncated"],
    )

    print("Dataset generated!")

    return ds


if __name__ == "__main__":
    dataset_name = "LunarLander_v2_test-dataset"

    print("\n Generate dataset as standard")
    generated_dataset = generate_dataset(dataset_name)

    print("\n Save dataset to local storage")
    generated_dataset.save()

    print(
        "\n Listing datasets in local storage, we should see the dataset we just generated"
    )
    minari.list_local_datasets()

    print("\n We can load the dataset from local storage as follows")
    loaded_dataset = minari.load_dataset(dataset_name)

    print("\n We can delete the dataset from local storage as follows")
    minari.delete_dataset(dataset_name)

    print(
        "\n Listing datasets in local storage, we should now no longer see the dataset we just generated"
    )
    minari.list_local_datasets()