Dian Chen (dotchen)

dotchen

Geek Repo

Company:UT Austin

Location:Austin, TX

Home Page:https://dotchen.github.io

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Dian Chen's repositories

LAV

(CVPR 2022) A minimalist, mapless, end-to-end self-driving stack for joint perception, prediction, planning and control.

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LearningByCheating

(CoRL 2019) Driving in CARLA using waypoint prediction and two-stage imitation learning

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WorldOnRails

(ICCV 2021, Oral) RL and distillation in CARLA using a factorized world model

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stock_pred

(Failed) attempts at predicting stock price movements with tweets and historical trends

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AdamW-pytorch

Implementation and experiment for AdamW on pytorch

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torchgeometry

TGM: PyTorch Geometry

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carla

Open-source simulator for autonomous driving research.

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convert_torch_to_pytorch

Convert torch t7 model to pytorch model and source.

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deepmask

Torch implementation of DeepMask and SharpMask

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dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination, implemented in PyTorch.

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fireplace

A Hearthstone simulator in Python

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gym

A toolkit for developing and comparing reinforcement learning algorithms.

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hw1

Imitation Learning Homework 1

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kmeans_pytorch

kmeans using PyTorch

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metastone

Hearthstone simulator written in Java with full GUI support

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models

Models built with TensorFlow

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mujoco-py

MuJoCo Python bindings

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pystk

Python SuperTuxKart interface

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recastnavigation

Navigation-mesh Toolset for Games

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rl

A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.

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rllab

rllab is a framework for developing and evaluating reinforcement learning algorithms, fully compatible with OpenAI Gym.

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Room-Allocation

A Django webapp that allocates room and divides house rent based on Spernel Labeling

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scenario_runner

Traffic scenario definition and execution engine

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segbyexp

Website for Learning Instance Segmentation for Experimentation

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stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

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train-procgen

Code for the paper "Leveraging Procedural Generation to Benchmark Reinforcement Learning"

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