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2021_rlkr_drone_delivery_challenge_with_unity

2021 RL Korea Drone Delivery Challenge with Unity

angrybird-pcgrl

A framework to train a PCG agent in AngryBird game

bcq

Author's PyTorch implementation of BCQ for continuous and discrete actions

bicnet

Bidirectionally-Coordinated Net Implements with PyTorch 1.0

chatgpt4pcg-node

This package contains a comprehensive set of utility functions utilized in scripts for the competition's evaluation process with a focus on Node.js-specific file utilities.

chatpcg

ChatPCG: Large Language Model-Driven Reward Design for Procedural Content Generation

ckeditor5-build-classic

The classic editor build of CKEditor 5.

control-pcgrl

Train or evolve controllable and diverse level-generators.

cooperative-overcooked-pcg

Implementation of the paper, 'Toward Cooperative Level Generation in Multiplayer Game'

cs_sockettest

donkey_offroad_rl

donkeycar

Open source hardware and software platform to build a small scale self driving car.

emoji-cheat-sheet

A markdown version emoji cheat sheet

grpc_go_python

gym-super-mario-bros

An OpenAI Gym interface to Super Mario Bros. & Super Mario Bros. 2 (Lost Levels) on The NES

interactive-poster

isekai-navi

jquery-custom-select

Custom Select jQuery Plugin

lab-management

lambda_kicad5_lib

Lambda Series Kicad5 Lib

maddpg_simpletag

Pytorch 1.0 MADDPG Implemente for simple_tag environment (OpenAI's)

mario_rl

RL algorithms for solving supermario game in gym environment

multiagent-particle-envs

Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"

multiplayer-turingtest

omokgame-with-computer

OmokGame with Computer

openai-unity

An unofficial OpenAI Unity Package that aims to help you use OpenAI API directly in Unity Game engine.

overcooked-ai

Offline Multi-Agent Reinforcement Learning Implementations: Solving Overcooked Game with Data-Driven Method

particleenv-multiagent-offlinerl

pcg-papers

Procedural Content Generation (PCG) papers summarization for newbies :)

per

Prioritized Experience Replay (PER) implementation in PyTorch