peihongyu Goto Github PK

airsimdrone

artificial-intelligence-berkeley-cs188

Learned about search problems (A*, CSP, minimax), reinforcement learning, bayes nets, hidden markov models, and machine learning

dl_pgo

dm2

This is the codebase for the paper, "DM2: Distributed Multi-Agent Reinforcement Learning via Distribution Matching".

enae788m_projects

epymarl

An extension of the PyMARL codebase that includes additional algorithms and environment support

gym-minigrid

Minimalistic gridworld package for OpenAI Gym

lightfieldrenderer

A Cubic Light Field Renderer

maml_rl

Code for RL experiments in "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

marl-arl

motion_planning_hws

mrn

Official codebase for "Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning" (NeurIPS 2022).

multi-agent-communication-considering-representation-learning

multiagent-particle-envs

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

peihongyu.com-neat

a compact person website template from https://github.com/yaoyao-liu/minimal-light

ppo-pytorch

Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

surreal

Open-Source Distributed Reinforcement Learning Framework by Stanford Vision and Learning Lab

ucb_cs285_homework_fall2020

Assignments for Berkeley CS 285: Deep Reinforcement Learning (Fall 2020)

ucberkeley_cs188_projects