RongjianLiang

aircraft_trajectory_optimisation

A simulation project using techniques in reinforcement learning to generate optimal paths for unmanned aircraft.

caffe

Caffe: a fast open framework for deep learning.

CS3211_assignment_1

Deep-Reinforcement-Learning-Algorithms-with-PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

DeepReinforcementLearningInAction

Code from the Deep Reinforcement Learning in Action book from Manning, Inc

DroneReach

DroneReachContinuous

A mutli-agent environment that simulates multiple drones chasing single or multiple evading goals, in a clutterred environment with moving obstacles.

hello-world

This is a repository to store ideas, resources, or even share and discuss things with others.

How-to-learn-Deep-Learning

A top-down, practical guide to learn AI, Deep learning and Machine Learning.

MAAC

Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019

RongjianLiang/aircraft_trajectory_optimisation

A simulation project using techniques in reinforcement learning to generate optimal paths for unmanned aircraft.

RongjianLiang/caffe

Caffe: a fast open framework for deep learning.

RongjianLiang/CS3211_assignment_1

RongjianLiang/Deep-Reinforcement-Learning-Algorithms-with-PyTorch

PyTorch implementations of deep reinforcement learning algorithms and environments

RongjianLiang/DeepReinforcementLearningInAction

Code from the Deep Reinforcement Learning in Action book from Manning, Inc

RongjianLiang/DroneReach

RongjianLiang/DroneReachContinuous

A mutli-agent environment that simulates multiple drones chasing single or multiple evading goals, in a clutterred environment with moving obstacles.

RongjianLiang/hello-world

This is a repository to store ideas, resources, or even share and discuss things with others.

RongjianLiang/How-to-learn-Deep-Learning

A top-down, practical guide to learn AI, Deep learning and Machine Learning.

RongjianLiang/MAAC

Code for "Actor-Attention-Critic for Multi-Agent Reinforcement Learning" ICML 2019

RongjianLiang/multiagent-particle-envs

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

RongjianLiang/on-policy

This is the official implementation of Multi-Agent PPO (MAPPO).

RongjianLiang/PPO_example

This is the code for demonstrating PPO algorithm in ant environment.

RongjianLiang/PPO_GAE

This is a demonstration of Proximal Policy Optimization with Generalized Advantage Estimate, using gym environment.

RongjianLiang/quad-swarm-rl

Additional environments compatible with OpenAI gym

RongjianLiang/SENet

Squeeze-and-Excitation Networks

RongjianLiang/Single_Agent_FOMDP

This is the Python files for Single Agent FOMDP project. Original code is provided in Jupyter notebook file format.

RongjianLiang/TF-transformer-tutorial

Making text a first-class citizen in TensorFlow.

RongjianLiang/UROP-Machine-Learning-project

This is a repository to store my codes for my UROP project in AY2019-20, under the supervision of Prof Shen Lei