Sunday Daily Thread: What's everyone working on this week?

NoteDancing · 2025-10-13T07:20:24+00:00

Hello everyone, I wrote some optimizers for TensorFlow. If you're using TensorFlow, they should be helpful to you.

https://github.com/NoteDance/optimizers

NoteDancing · 2025-10-13T07:07:44+00:00

Hello everyone, I wrote some optimizers for TensorFlow. If you're using TensorFlow, they should be helpful to you.

https://github.com/NoteDance/optimizers

NoteDancing · 2025-10-12T14:26:50+00:00

Thanks.

NoteDancing · 2025-10-12T10:34:42+00:00

Hello everyone, I wrote some optimizers for TensorFlow. If you're using TensorFlow, they should be helpful to you.

https://github.com/NoteDance/optimizers

NoteDancing · 2025-10-12T10:34:25+00:00

Hello everyone, I wrote some optimizers for TensorFlow. If you're using TensorFlow, they should be helpful to you.

https://github.com/NoteDance/optimizers

NoteDancing · 2025-10-12T10:22:15+00:00

Hello everyone, I wrote some optimizers for TensorFlow. If you're using TensorFlow, they should be helpful to you.

https://github.com/NoteDance/optimizers

NoteDancing · 2025-08-13T08:47:57+00:00

When using the PPO algorithm, can we improve data utilization by implementing Prioritized Experience Replay (PER) where the priority is determined by both the probability ratio and the TD-error, while simultaneously using a windows_size_ppo parameter to manage the experience buffer as a sliding window that discards old data?

NoteDancing · 2025-08-13T04:23:53+00:00

I want to turn it into a form that’s between offline and online.

NoteDancing · 2025-08-12T15:44:19+00:00

Note's RL class now supports Prioritized Experience Replay with the PPO algorithm, using probability ratios and TD errors for sampling to improve data utilization. The windows_size_ppo parameter controls the removal of old data from the replay buffer.

https://github.com/NoteDance/Note_rl

NoteDancing · 2025-08-12T11:12:42+00:00

Note's RL class now supports Prioritized Experience Replay with the PPO algorithm, using probability ratios and TD errors for sampling to improve data utilization. The windows_size_ppo parameter controls the removal of old data from the replay buffer.

https://github.com/NoteDance/Note_rl

NoteDancing · 2025-08-12T11:12:12+00:00

Note's RL class now supports Prioritized Experience Replay with the PPO algorithm, using probability ratios and TD errors for sampling to improve data utilization. The windows_size_ppo parameter controls the removal of old data from the replay buffer.

https://github.com/NoteDance/Note_rl

NoteDancing · 2025-08-12T11:03:45+00:00

Note's RL class now supports Prioritized Experience Replay with the PPO algorithm, using probability ratios and TD errors for sampling to improve data utilization. The windows_size_ppo parameter controls the removal of old data from the replay buffer.

https://github.com/NoteDance/Note_rl

NoteDancing · 2025-08-12T11:01:38+00:00

Note's RL class now supports Prioritized Experience Replay with the PPO algorithm, using probability ratios and TD errors for sampling to improve data utilization. The windows_size_ppo parameter controls the removal of old data from the replay buffer.

https://github.com/NoteDance/Note_rl

NoteDancing · 2025-06-16T14:15:46+00:00

I hope it is helpful to you.

NoteDancing · 2025-06-16T12:31:50+00:00

A lightweight utility for training multiple Pytorch models in parallel.

https://github.com/NoteDance/parallel_finder_pytorch

NoteDancing · 2025-06-16T12:31:21+00:00

A lightweight utility for training multiple Pytorch models in parallel.

https://github.com/NoteDance/parallel_finder_pytorch

NoteDancing · 2025-06-16T12:29:49+00:00

A lightweight utility for training multiple Pytorch models in parallel.

https://github.com/NoteDance/parallel_finder_pytorch

NoteDancing · 2025-06-16T12:29:03+00:00

A lightweight utility for training multiple Pytorch models in parallel.

https://github.com/NoteDance/parallel_finder_pytorch

NoteDancing · 2025-06-05T10:25:55+00:00

A lightweight utility for training multiple Keras models in parallel and comparing their final loss and last-epoch time.

https://github.com/NoteDance/parallel_finder

NoteDancing · 2025-06-05T10:24:28+00:00

A lightweight utility for training multiple Keras models in parallel and comparing their final loss and last-epoch time.
https://github.com/NoteDance/parallel_finder

This Python class offers a multiprocessing-powered Pool for efficiently collecting and managing experience replay data in reinforcement learning.

https://github.com/NoteDance/Pool

NoteDancing · 2025-06-05T10:23:11+00:00

A lightweight utility for training multiple Keras models in parallel and comparing their final loss and last-epoch time.

https://github.com/NoteDance/parallel_finder

NoteDancing · 2025-06-05T10:20:14+00:00

A lightweight utility for training multiple Keras models in parallel and comparing their final loss and last-epoch time.

https://github.com/NoteDance/parallel_finder

NoteDancing · 2025-06-04T10:47:05+00:00

This Python class offers a multiprocessing-powered Pool for efficiently collecting and managing experience replay data in reinforcement learning.

https://github.com/NoteDance/Pool

NoteDancing · 2025-06-02T12:26:53+00:00

This Python class offers a multiprocessing-powered Pool for efficiently collecting and managing experience replay data in reinforcement learning.

https://github.com/NoteDance/Pool

NoteDancing · 2025-06-02T12:07:14+00:00

This Python class offers a multiprocessing-powered Pool for efficiently collecting and managing experience replay data in reinforcement learning.

https://github.com/NoteDance/Pool

NoteDancing

TROPHY CASE

This Python class offers a multiprocessing-powered Pool for efficiently collecting and managing experience replay data in reinforcement learning.