Robinaoko

Robinaoko's Stars

• Farama-Foundation/Minigrid

  Simple and easily configurable grid world environments for reinforcement learning

  Language:Python2.1k609

• PxGame/XMLib.AM

  XMLib 动作游戏开发套件

  Language:C#592158

• Leantha/COMS7053A_Assignment

  Language:Python1

• JustinAbrams/RL_assignment

  MiniHack RL models for Quest-Hard-v0

  Language:Python1

• mikevogt/RL-Project--Minihack-Quest-Hard-v0

  This Github repository serves as the source code for completing the Minihack-Quest-Hard-v0 using Reinforcement Learning

  Language:Python1

• BrentonBudler/deep-rl-minihack-the-planet

  Learning Minihack environments using DQN and Actor-Critic architectures

  Language:Jupyter Notebook1

• EllanJiang/GameFramework

  This is literally a game framework, based on Unity game engine. It encapsulates commonly used game modules during development, and, to a large degree, standardises the process, enhances the development speed and ensures the product quality.

  Language:C#5.9k1.4k

• BY571/DQN-Atari-Agents

  DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN

  Language:Jupyter Notebook12014

• UnityRPG/2-Core-Combat

  The second section of the course. You will learn everything required to build simple core combat in your RPG, creating the core experience.(REF: CC_RPG) http://gdev.tv/rpggithub

  Language:ShaderLab6026

• snumrl/TimeCriticalResponse

  Learning Time-Critical Responses for Interactive Character Control

  Language:Java24136

• chrisloop/Landscape7

  Language:C#173

• epignatelli/human-level-control-through-deep-reinforcement-learning

  A jax/stax implementation of: Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G. and Petersen, S., 2015. Human-level control through deep reinforcement learning. nature, 518(7540), pp.529-533.

  Language:Python74

• mixandjam/FFVII-TacticalMode

  Recreating the Tactical Mode mechanic from Final Fantasy VII Remake

  Language:C#15260

• Unity-Technologies/ml-agents

  The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

  Language:C#17.2k4.2k

• lettier/3d-game-shaders-for-beginners

  🎮 A step-by-step guide to implementing SSAO, depth of field, lighting, normal mapping, and more for your 3D game.

  Language:C++17.9k1.4k

• XINCGer/Unity3DTraining

  【Unity杂货铺】unity大杂烩~

  Language:C#7.7k2k

• 664235822/DarkSouls-Demo

  根据教程学习制作黑暗之魂游戏Demo

  Language:C#5324

• ColinLeung-NiloCat/UnityURPToonLitShaderExample

  A very simple toon lit shader example, for you to learn writing custom lit shader in Unity URP

  Language:ShaderLab6.4k1.7k

• higgsfield/RL-Adventure

  Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

  Language:Jupyter Notebook3k589

• ucla-rlcourse/DeepRL-Tutorials

  Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

  Language:Jupyter Notebook25091

• ucla-rlcourse/RLexample

  Some basic examples of playing with RL

  Language:Python1.2k300

• google-deepmind/dm_env

  A Python interface for reinforcement learning environments

  Language:Python34644

• wenzhe-li/romi

  Code for NeurIPS 2021 paper "Offline Reinforcement Learning with Reverse Model-based Imagination"

  Language:Python183

• apexrl/bmpo

  Implementation of ICML2020 paper <Bidirectional Model-based Policy Optimization>

  Language:Python23

• datawhalechina/easy-rl

  强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

  Language:Jupyter Notebook9.5k1.9k

• LASP-UCL/Student-project-quickstart

  A quick guide to get start on a student project

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• Seeed-Studio/Sketchbook_Starter_Kit_V2.0

  Language:C++10470

• Seeed-Studio/Sketchbook_Starter_Kit_for_Arduino

  Language:C++7345

• openai/gym

  A toolkit for developing and comparing reinforcement learning algorithms.

  Language:Python34.8k8.6k

• cwfparsonson/AMLS_II

  This code is for use within the UCL Electronic Engineering AMLS II module (ELEC0135).

  Language:Jupyter Notebook108