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ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments...
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari2600 game-playing (Bellemare et al., 2013). Specifically, our Rainbow agent implements the three components identified as most important...
Atari2600, C64, Python, 3D engines and rock'n roll
MVE is an acronym for Modular Virtual Engine. It provides an interface for programming and running (your own) units (virtual machines), each unit consisting of several modules (CPU, Video, Audio, Input, Drive, et cetera). Furthermore the Modular Virtual.
This project has been frozen because of lack of motivation.
But what you will find here, is a lot assembly code for the Atari2600 and C64, experiments with 3D math on Python.
Squish65 is a 6507 assembly optimizer developed for use with batari Basic in making Atari2600 games, but also intended to be useful for general 650x DASM assembler programming.
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