yangzhao-666
PhD student @ LIACS, and doing reinforcement learning.
LIACS, Leiden UniversityLeiden, the NL
Pinned Repositories
2m
Code for "Two-Memory Reinforcement Learning", COG 2023. A general framework to combine non-parametric episodic memory method and parametric deep reinforcement learning method.
cec
Code for "Continuous Episodic Control", COG 2023. A non-parametric method for continuous control tasks.
common-mujoco-errors
This is a repository intends to summarise common errors people encounter while setting up mujoco_py experiments.
medal
Code to reproduce results for MEDAL in PyTorch. Also contains code for running SAC and FBRL.
PbRSS
The code used for BNAIC 2021 paper "Potential-based Reward Shaping in Sokoban"
Reinforcement-Learning
This is the assignment of RL, Leiden University
sample-efficient-bayesian-rl
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL
TLCLS
The code used for BNAIC 2021 paper "Transfer Learning and Curriculum Learning in Sokoban"
yangzhao-666.github.io
Personal webpage of Zhao Yang, originally forked from Kenneth Li
YoungChat
因毕业设计需要，计划使用mfc编写一款IM
yangzhao-666's Repositories
yangzhao-666/YoungChat
因毕业设计需要，计划使用mfc编写一款IM
yangzhao-666/cec
Code for "Continuous Episodic Control", COG 2023. A non-parametric method for continuous control tasks.
yangzhao-666/PbRSS
The code used for BNAIC 2021 paper "Potential-based Reward Shaping in Sokoban"
yangzhao-666/2m
Code for "Two-Memory Reinforcement Learning", COG 2023. A general framework to combine non-parametric episodic memory method and parametric deep reinforcement learning method.
yangzhao-666/TLCLS
The code used for BNAIC 2021 paper "Transfer Learning and Curriculum Learning in Sokoban"
yangzhao-666/common-mujoco-errors
This is a repository intends to summarise common errors people encounter while setting up mujoco_py experiments.
yangzhao-666/medal
Code to reproduce results for MEDAL in PyTorch. Also contains code for running SAC and FBRL.
yangzhao-666/Reinforcement-Learning
This is the assignment of RL, Leiden University
yangzhao-666/sample-efficient-bayesian-rl
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL
yangzhao-666/yangzhao-666.github.io
Personal webpage of Zhao Yang, originally forked from Kenneth Li