Bowen Fang

Bowen Fang

PhD student at Columbia University, focusing on Reinforcement Learning and Autonomous Driving research.

Posts by Tags

DL

What are the Effective Deep Learning Models for Tabular Data?

27 minute read

Published:

This week, I would like to share a paper published at NeurIPS 2021. When dealing with tabular data, I often find myself perplexed. On one hand, I am unsure which deep learning frameworks are better suited for this task, and on the other hand, I am uncertain whether the time-consuming process of training a model can outperform the easily accessible GBDT family of models such as XGBoost and LightGBM. However, this paper provides a detailed and comprehensive comparison of deep learning algorithms and GBDT models on tabular data. It introduces new baselines and presents a novel architecture that outperforms other deep learning models. I have gained a lot from this paper and would like to share it with you.

Differentiable

MPC with a Differentiable Forward Model: An Implementation with Jax

11 minute read

Published:

mpc control

Intro

In a recent project for MECS6616 Robot Learning, I got hands-on experience for Model Predictive Control (MPC). To solve the problem, the use of constant action and pseudo-gradient is a recommended method, and it truly provides simple yet good enough solutions. However, the project instructions also hinted at another prospect: a differentiable forward model could help, since you can always compute numerical gradients. This piqued my curiosity - could we directly compute the gradient with respect to action given the evaluation metric? And if so, how could we implement this practically?

EfficientZero

HER

“Hindsight” – An easy yet effective RL Technique HER with Pytorch implementation

22 minute read

Published:

This week, I will share a paper published by OpenAI at NeurIPS 2017. The ideas presented in this paper are quite insightful, and it tackles a complex problem using only simple algorithmic improvements. I gained significant inspiration from this paper. At the end, I will also provide a brief implementation of HER (Hindsight Experience Replay).

HFT

Will DRL Make Profit in High-Frequency Trading?

10 minute read

Published:

Can deep reinforcement learning algorithms be used to train a trading agent that can achieve long-term profitability using Limit Order Book (LOB) data? To answer this question, this article proposes a deep reinforcement learning framework for high-frequency trading and conducts experiments using limit order data from LOBSTER with the PPO algorithm. The results show that the agent is able to identify short-term patterns in the data and propose profitable trading strategies.

JAX

MPC with a Differentiable Forward Model: An Implementation with Jax

11 minute read

Published:

mpc control

Intro

In a recent project for MECS6616 Robot Learning, I got hands-on experience for Model Predictive Control (MPC). To solve the problem, the use of constant action and pseudo-gradient is a recommended method, and it truly provides simple yet good enough solutions. However, the project instructions also hinted at another prospect: a differentiable forward model could help, since you can always compute numerical gradients. This piqued my curiosity - could we directly compute the gradient with respect to action given the evaluation metric? And if so, how could we implement this practically?

LOB

Will DRL Make Profit in High-Frequency Trading?

10 minute read

Published:

Can deep reinforcement learning algorithms be used to train a trading agent that can achieve long-term profitability using Limit Order Book (LOB) data? To answer this question, this article proposes a deep reinforcement learning framework for high-frequency trading and conducts experiments using limit order data from LOBSTER with the PPO algorithm. The results show that the agent is able to identify short-term patterns in the data and propose profitable trading strategies.

MCTS

MPC

MPC with a Differentiable Forward Model: An Implementation with Jax

11 minute read

Published:

mpc control

Intro

In a recent project for MECS6616 Robot Learning, I got hands-on experience for Model Predictive Control (MPC). To solve the problem, the use of constant action and pseudo-gradient is a recommended method, and it truly provides simple yet good enough solutions. However, the project instructions also hinted at another prospect: a differentiable forward model could help, since you can always compute numerical gradients. This piqued my curiosity - could we directly compute the gradient with respect to action given the evaluation metric? And if so, how could we implement this practically?

MuZero

Pytorch

“Hindsight” – An easy yet effective RL Technique HER with Pytorch implementation

22 minute read

Published:

This week, I will share a paper published by OpenAI at NeurIPS 2017. The ideas presented in this paper are quite insightful, and it tackles a complex problem using only simple algorithmic improvements. I gained significant inspiration from this paper. At the end, I will also provide a brief implementation of HER (Hindsight Experience Replay).

What are the Effective Deep Learning Models for Tabular Data?

27 minute read

Published:

This week, I would like to share a paper published at NeurIPS 2021. When dealing with tabular data, I often find myself perplexed. On one hand, I am unsure which deep learning frameworks are better suited for this task, and on the other hand, I am uncertain whether the time-consuming process of training a model can outperform the easily accessible GBDT family of models such as XGBoost and LightGBM. However, this paper provides a detailed and comprehensive comparison of deep learning algorithms and GBDT models on tabular data. It introduces new baselines and presents a novel architecture that outperforms other deep learning models. I have gained a lot from this paper and would like to share it with you.

RL

“Hindsight” – An easy yet effective RL Technique HER with Pytorch implementation

22 minute read

Published:

This week, I will share a paper published by OpenAI at NeurIPS 2017. The ideas presented in this paper are quite insightful, and it tackles a complex problem using only simple algorithmic improvements. I gained significant inspiration from this paper. At the end, I will also provide a brief implementation of HER (Hindsight Experience Replay).

Will DRL Make Profit in High-Frequency Trading?

10 minute read

Published:

Can deep reinforcement learning algorithms be used to train a trading agent that can achieve long-term profitability using Limit Order Book (LOB) data? To answer this question, this article proposes a deep reinforcement learning framework for high-frequency trading and conducts experiments using limit order data from LOBSTER with the PPO algorithm. The results show that the agent is able to identify short-term patterns in the data and propose profitable trading strategies.

Robot Learning

MPC with a Differentiable Forward Model: An Implementation with Jax

11 minute read

Published:

mpc control

Intro

In a recent project for MECS6616 Robot Learning, I got hands-on experience for Model Predictive Control (MPC). To solve the problem, the use of constant action and pseudo-gradient is a recommended method, and it truly provides simple yet good enough solutions. However, the project instructions also hinted at another prospect: a differentiable forward model could help, since you can always compute numerical gradients. This piqued my curiosity - could we directly compute the gradient with respect to action given the evaluation metric? And if so, how could we implement this practically?

SampledMuZero

StochasticMuZero

Tabular Data

What are the Effective Deep Learning Models for Tabular Data?

27 minute read

Published:

This week, I would like to share a paper published at NeurIPS 2021. When dealing with tabular data, I often find myself perplexed. On one hand, I am unsure which deep learning frameworks are better suited for this task, and on the other hand, I am uncertain whether the time-consuming process of training a model can outperform the easily accessible GBDT family of models such as XGBoost and LightGBM. However, this paper provides a detailed and comprehensive comparison of deep learning algorithms and GBDT models on tabular data. It introduces new baselines and presents a novel architecture that outperforms other deep learning models. I have gained a lot from this paper and would like to share it with you.

Transformer

What are the Effective Deep Learning Models for Tabular Data?

27 minute read

Published:

This week, I would like to share a paper published at NeurIPS 2021. When dealing with tabular data, I often find myself perplexed. On one hand, I am unsure which deep learning frameworks are better suited for this task, and on the other hand, I am uncertain whether the time-consuming process of training a model can outperform the easily accessible GBDT family of models such as XGBoost and LightGBM. However, this paper provides a detailed and comprehensive comparison of deep learning algorithms and GBDT models on tabular data. It introduces new baselines and presents a novel architecture that outperforms other deep learning models. I have gained a lot from this paper and would like to share it with you.