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behaviac

behaviac is a framework of the game AI development, and it also can be used as a rapid game prototype design tool. behaviac supports the behavior tree, finite state machine and hierarchical task network(BT, FSM, HTN)

bezos

:godmode: Bezos: Build your own Reinforcement Learning framework

bitcoinbook

Mastering Bitcoin 2nd Edition - Programming the Open Blockchain

brain-tokyo-workshop

🧠🗼

brainstorm

Fast, flexible and fun neural networks.

bsuite

bsuite is a collection of carefully-designed experiments that investigate core capabilities of a reinforcement learning (RL) agent

bullet3

Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.

bytelegend

Enjoy programming while playing a game.

cactus-maml

Code for Unsupervised Learning via Meta-Learning.

cactus-protonets

Code for Unsupervised Learning via Meta-Learning.

calibratedmodelbasedrl

Code for "Calibrated Model-Based Deep Reinforcement Learning", ICML 2019.

cardboard_simpletest

just a cardboard game.就是一个简单的居于cardboard的游戏。请找到旋转的cubu然后拖动磁环，然后继续找。

carla-rl

Environments and Wrappers for CARLA, designed for ease of use with RL Tasks.

casl

TensorFlow Implementation of Crossmodal Attentive Skill Learner (CASL)

categorical-dqn

A working implementation of the Categorical DQN (Distributional RL).

cavia

Code for "Fast Context Adaptation via Meta-Learning"

celluloid

:movie_camera: Matplotlib animations made easy

chainerrl

ChainerRL is a deep reinforcement learning library built on top of Chainer.

challenge-aido_lf-baseline-rl-sim-pytorch

cher

Curriculum-guided Hindsight Experience Replay (NeurIPS-2019)

cityflow

A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

citysim3d

CitySim3D: Simulated car following benchmark

cleverhans

An adversarial example library for constructing attacks, building defenses, and benchmarking both

coach

Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

coax

Turn RL papers into code, the easy way

code-for-paper

coiltraine

Training framework for conditional imitation learning

coinrun

Code for the paper "Quantifying Transfer in Reinforcement Learning"

combined-experience-replay

A Deeper Look at Experience Replay (Zhang and Sutton, 2017)

commentbox

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