A wrapper for Minari environments.

This crate provides a Rust interface for Minari datasets, which are collections of offline reinforcement learning data. It allows users to load and interact with Minari datasets in a way that is compatible with the Border framework.

Features

• Dataset Loading: Load Minari datasets from disk or from the Minari registry.
• Environment Interaction: Interact with the loaded datasets using the Border environment interface.
• Data Access: Access observations, actions, rewards, and other data from the datasets.

Example

The following example demonstrates how to:

1. Load a D4RL Kitchen dataset
2. Create a replay buffer from a specific episode
3. Recover the environment state
4. Replay the actions from the dataset

This is particularly useful for:

• Analyzing expert demonstrations
• Testing environment behavior
• Validating dataset quality
• Reproducing recorded trajectories

use border_core::Env;
use border_minari::{d4rl::kitchen::ndarray::KitchenConverter, MinariDataset};

fn main() -> Result<()> {
    // Load the D4RL Kitchen dataset
    let dataset = MinariDataset::load_dataset("D4RL/kitchen/complete-v1", true)?;

    // Create a converter for handling observation and action types
    let mut converter = KitchenConverter {};

    // Create a replay buffer containing only the sixth episode
    let replay_buffer = dataset.create_replay_buffer(&mut converter, Some(vec![5]))?;

    // Recover the environment state from the dataset
    // The 'false' parameter indicates not to use the initial state
    // 'human' indicates the agent type
    let mut env = dataset.recover_environment(converter, false, "human")?;

    // Get the sequence of actions from the replay buffer
    let actions = replay_buffer.whole_actions();

    // Reset the environment and replay the actions
    env.reset(None)?;
    for ix in 0..actions.action.shape()[0] {
        let act = actions.get(ix);
        let _ = env.step(&act);
    }

    Ok(())
}

The example uses the following key components:

• KitchenConverter: Handles conversion between Python and Rust types for the Kitchen environment
• MinariDataset: Manages the dataset and provides methods for data access
• Env: The Border environment interface for interaction

Integration with Border

This crate implements the Env trait from border-core, making it compatible with other Border components such as agents, policies, and trainers. It can be used in both online and offline reinforcement learning scenarios.