Autonomous-Learning-Library

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autonomous-learning-library is a PyTorch library for building deep reinforcement learning agents. It provides flexible components for quickly building and evaluating novel reinforcement learning agents and offers high-quality reference implementations of modern DRL algorithms.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is autonomous-learning-library?

The autonomous-learning-library is an object-oriented deep reinforcement learning (DRL) library built on PyTorch, designed to facilitate the rapid development and evaluation of new reinforcement learning agents. It offers a flexible function approximation API with features like target networks, gradient clipping, learning rate schedules, and multi-headed networks. The library includes various memory buffers, such as prioritized experience replay and generalized advantage estimation, and a PyTorch-based environment interface. It also provides common wrappers and agent enhancements for standard benchmarks, Slurm integration for large-scale experiments, and plotting/logging utilities with Tensorboard integration. The library separates agents into high-level implementations and specific presets for environments like Atari games, classic control tasks, and robotics simulations, with benchmark results on par with published research.

Best used for

Ideal for developers who need to rapidly prototype and test new deep reinforcement learning agents, evaluate existing DRL algorithms, and conduct large-scale experiments. Especially valuable for researchers and practitioners working with PyTorch in the field of reinforcement learning.

Common actions

build reinforcement learning agents

evaluate DRL algorithms

implement deep learning models

manage machine learning experiments

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Capabilities

Key features

PyTorch DRL library
Flexible approximation API
Memory buffers
Torch-based environment
Slurm integration
Tensorboard logging
Reference DRL algorithms

Target Audience

developer

Integrations

pytorchtensorboardslurm

Pricing & Plans

Open Source

Free

FAQs

What deep reinforcement learning algorithms are implemented in autonomous-learning-library?

The library includes implementations of Advantage Actor-Critic (A2C), Categorical DQN (C51), Deep Deterministic Policy Gradient (DDPG), Deep Q-Learning (DQN) with extensions, Proximal Policy Optimization (PPO), Rainbow, and Soft Actor-Critic (SAC). It also provides vanilla agents like Actor-Critic, Policy Gradient, Q-Learning, and Sarsa.

How can I install the autonomous-learning-library?

You can install the core library via pip using `pip install autonomous-learning-library`. For additional extras like Gym environments, use `pip install autonomous-learning-library[all]`. You can also install directly from the GitHub repository for development purposes.

Does the library support running large-scale experiments?

Yes, the autonomous-learning-library includes built-in Slurm integration, which is designed to facilitate running large-scale experiments efficiently. This allows users to manage and execute complex reinforcement learning tasks across multiple computing resources.

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