DIAMOND

A reinforcement learning agent trained in a diffusion world model

CommonProductProductivityMachine LearningReinforcement Learning

DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model for visually-rich worlds crucial to Atari games. Trained on a subset of Atari games using autoregressive imagination, it offers quick installation and allows users to experiment with pre-trained world models.

Autoregressive imagination applied to a subset of Atari games
Quick installation and experimentation with pre-trained world models
Environment configuration using miniconda or python venv
Supports various control methods
such as pressing 'm' to gain control
Allows adjustment of diffusion world model sampling parameters
Provides visualization and dataset modes to browse and replay stored episodes

DIAMOND is targeted towards machine learning researchers
reinforcement learning enthusiasts
and developers interested in artificial intelligence applications in gaming. The DIAMOND model can help them understand and apply diffusion models in reinforcement learning
as well as how to leverage autoregressive imagination to enhance game strategies.

Researchers utilize the DIAMOND model for strategy training and evaluation in Atari games.
Developers leverage DIAMOND for autoregressive imagination of game environments to improve AI agents.
Educators employ DIAMOND as a teaching case study
demonstrating reinforcement learning applications in real-world problems.

Clone or download the DIAMOND code repository to your local machine.
Configure the development environment using miniconda or python venv as per the provided installation guidelines.
Install the required dependencies
such as Python 3.10 and other libraries.
Run the pre-trained world model to observe the agent's performance.

Visit

DIAMOND Visit Over Time

Monthly Visits

515580771

Bounce Rate

37.20%

Page per Visit

5.8

Visit Duration

00:06:42

DIAMOND Visit Trend

DIAMOND Visit Geography

DIAMOND Traffic Sources

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