¿Cuáles son las características principales de google-deepmind/acme?

Las características principales de google-deepmind/acme son: Distributed RL Scaling, Reinforcement Learning, Distributed, RL Agent Implementation Frameworks, Agentic Reinforcement Learning, Reinforcement Learning Agents, Distributed Execution Scaling, RL Algorithm Benchmarking Toolkits.

¿Qué alternativas de código abierto existen para google-deepmind/acme?

Las alternativas de código abierto para google-deepmind/acme incluyen: vwxyzjn/cleanrl — CleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations… keras-rl/keras-rl — keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a… google/dopamine — Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse… rlinf/rlinf — RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the… dlr-rm/rl-baselines3-zoo — This project is a collection of pretrained reinforcement learning agents and training scripts built on Stable… dlr-rm/stable-baselines3 — Stable-baselines3 is a reinforcement learning library built on the PyTorch deep learning framework. It provides a…

google-deepmindacme

Name: google-deepmind/acme
Author: google-deepmind

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4,005 estrellas541 forksPythonApache-2.03 vistas

Acme

Acme es un framework de aprendizaje por refuerzo y motor de ejecución diseñado para desarrollar y realizar benchmarks de algoritmos de aprendizaje. Proporciona una biblioteca de componentes modulares e implementaciones de referencia utilizadas para construir agentes y establecer líneas base de rendimiento.

El sistema permite escalar arquitecturas de agentes desde la ejecución de flujo único hasta grandes entornos distribuidos. Esto facilita la transición desde el prototipado inicial hasta la ejecución distribuida para entrenamiento y evaluación.

El framework cubre el desarrollo de aprendizaje por refuerzo y el prototipado de arquitecturas de agentes, proporcionando los bloques de construcción necesarios para comparar nuevos modelos frente a agentes de referencia estándar.

Features

Distributed RL Scaling - Acts as an execution engine for scaling reinforcement learning training and rollout generation across distributed GPU nodes.
Reinforcement Learning - Offers a comprehensive framework for training agents through trial and error using modular components.
Distributed - Scales reinforcement learning workflows from single-stream processes to large distributed systems.
RL Agent Implementation Frameworks - Provides the structural support and modular building blocks necessary for implementing RL agents.
Agentic Reinforcement Learning - Provides a specialized framework for developing and training agents using reinforcement learning.
Reinforcement Learning Agents - Provides a library of reference implementations for constructing reinforcement learning agents.
Distributed Execution Scaling - Enables scaling agent architectures from single-stream execution to large distributed environments.
RL Algorithm Benchmarking Toolkits - Implements toolkits for benchmarking new reinforcement learning algorithms against standard reference agents.
Agent Architectures - Supports the design and iteration of structural patterns for reinforcement learning agents.
Reinforcement Learning - Library of reinforcement learning components and agents.

Historial de estrellas

google-deepmindacme

View on GitHub

4,005 estrellas541 forksPythonApache-2.03 vistas

Acme

Features

Distributed RL Scaling - Acts as an execution engine for scaling reinforcement learning training and rollout generation across distributed GPU nodes.
Reinforcement Learning - Offers a comprehensive framework for training agents through trial and error using modular components.
Distributed - Scales reinforcement learning workflows from single-stream processes to large distributed systems.
RL Agent Implementation Frameworks - Provides the structural support and modular building blocks necessary for implementing RL agents.
Agentic Reinforcement Learning - Provides a specialized framework for developing and training agents using reinforcement learning.
Reinforcement Learning Agents - Provides a library of reference implementations for constructing reinforcement learning agents.
Distributed Execution Scaling - Enables scaling agent architectures from single-stream execution to large distributed environments.
RL Algorithm Benchmarking Toolkits - Implements toolkits for benchmarking new reinforcement learning algorithms against standard reference agents.
Agent Architectures - Supports the design and iteration of structural patterns for reinforcement learning agents.
Reinforcement Learning - Library of reinforcement learning components and agents.

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vwxyzjn/cleanrl
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CleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations for online reinforcement learning algorithms. It serves as a deep reinforcement learning benchmark suite and experiment orchestrator designed for research and agent development across both discrete and continuous action spaces. The project is distinguished by its single-file algorithm implementation approach, which encapsulates each algorithm in a standalone script to eliminate complex class hierarchies. This structure is paired with a system for scheduling and executing large-s
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keras-rl/keras-rl
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google/dopamine
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Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse simulated environments. It provides an agent development toolkit that utilizes a flat class hierarchy to facilitate the creation and extension of learning agents. The framework includes a standardization layer via environment wrappers that connect agents to various physics simulations and gaming environments. It also features a high-performance experience replay buffer for storing and sampling transition data to improve training stability, alongside a dedicated hyperparameter
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rlinf/rlinf
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RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
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Ver las 30 alternativas a Acme

Preguntas frecuentes

¿Qué hace google-deepmind/acme?

Historial de estrellas

Alternativas open-source a Acme

Proyectos open-source similares, clasificados según cuántas características comparten con Acme.

vwxyzjn/cleanrl
vwxyzjn/cleanrl
9,127Ver en GitHub
CleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations for online reinforcement learning algorithms. It serves as a deep reinforcement learning benchmark suite and experiment orchestrator designed for research and agent development across both discrete and continuous action spaces. The project is distinguished by its single-file algorithm implementation approach, which encapsulates each algorithm in a standalone script to eliminate complex class hierarchies. This structure is paired with a system for scheduling and executing large-s
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keras-rl/keras-rl
keras-rl/keras-rl
5,551Ver en GitHub
keras-rl is a reinforcement learning library that enables the training of neural agents using Keras. It serves as a framework for implementing deep reinforcement learning agents that interact with simulated environments to discover optimal behaviors and maximize cumulative rewards. The library provides a system for configuring, training, and managing neural network agents. It handles the interaction loop between agents and environments, allowing models to learn through direct experience and gradient-based optimization. The framework includes capabilities for model weight management, allowing
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Ver en GitHub5,551
google/dopamine
google/dopamine
10,879Ver en GitHub
Dopamine is a reinforcement learning research framework designed for prototyping and testing algorithms across diverse simulated environments. It provides an agent development toolkit that utilizes a flat class hierarchy to facilitate the creation and extension of learning agents. The framework includes a standardization layer via environment wrappers that connect agents to various physics simulations and gaming environments. It also features a high-performance experience replay buffer for storing and sampling transition data to improve training stability, alongside a dedicated hyperparameter
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rlinf/rlinf
RLinf/RLinf
2,502Ver en GitHub
RLinf is a distributed reinforcement learning orchestrator and embodied AI training framework. It provides the infrastructure to train vision-language-action models and robotic policies using a combination of reinforcement learning and supervised fine-tuning. The system is designed for scaling workloads across GPU clusters, managing the placement of actors, rollout workers, and environment components. It features a specialized robotics data collection pipeline for gathering teleoperated demonstrations and simulation trajectories into standardized replay buffers, alongside a hardware interface
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Ver las 30 alternativas a Acme

Acme

Features

Historial de estrellas

Acme

Features

Alternativas open-source a Acme

vwxyzjn/cleanrl

keras-rl/keras-rl

google/dopamine

RLinf/RLinf

Preguntas frecuentes

Historial de estrellas

Alternativas open-source a Acme

vwxyzjn/cleanrl

keras-rl/keras-rl

google/dopamine

RLinf/RLinf

Preguntas frecuentes