This is the Linux app named RLax whose latest release can be downloaded as RLax0.1.8sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named RLax with OnWorks for free.
Ikut arahan ini untuk menjalankan apl ini:
- 1. Memuat turun aplikasi ini dalam PC anda.
- 2. Masukkan dalam pengurus fail kami https://www.onworks.net/myfiles.php?username=XXXXX dengan nama pengguna yang anda mahukan.
- 3. Muat naik aplikasi ini dalam pengurus filem tersebut.
- 4. Mulakan OnWorks Linux dalam talian atau emulator dalam talian Windows atau emulator dalam talian MACOS dari tapak web ini.
- 5. Daripada OS Linux OnWorks yang baru anda mulakan, pergi ke pengurus fail kami https://www.onworks.net/myfiles.php?username=XXXX dengan nama pengguna yang anda mahukan.
- 6. Muat turun aplikasi, pasang dan jalankan.
SKRIN:
RLax
HURAIAN:
RLax (pronounced “relax”) is a JAX-based library developed by Google DeepMind that provides reusable mathematical building blocks for constructing reinforcement learning (RL) agents. Rather than implementing full algorithms, RLax focuses on the core functional operations that underpin RL methods—such as computing value functions, returns, policy gradients, and loss terms—allowing researchers to flexibly assemble their own agents. It supports both on-policy and off-policy learning, as well as value-based, policy-based, and model-based approaches. RLax is fully JIT-compilable with JAX, enabling high-performance execution across CPU, GPU, and TPU backends. The library implements tools for Bellman equations, return distributions, general value functions, and policy optimization in both continuous and discrete action spaces. It integrates seamlessly with DeepMind’s Haiku (for neural network definition) and Optax (for optimization), making it a key component in modular RL pipelines.
Ciri-ciri
- Modular reinforcement learning primitives (values, returns, and policies)
- JAX-optimized for GPU/TPU acceleration and automatic differentiation
- Supports on-policy and off-policy learning paradigms
- Implements distributional value functions and general value functions
- Integrates with Haiku and Optax for neural network and optimization pipelines
- Comprehensive testing and examples for reproducibility and educational use
Bahasa Pengaturcaraan
Python, Unix Shell
Kategori
This is an application that can also be fetched from https://sourceforge.net/projects/rlax.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.