A3C-LSTM
A Python implementation of the A3C reinforcement learning algorithm with an LSTM network, tested on the CartPole environment. The readme notes it does not converge and points to a DDPG version.
Share on XNo license declared
Overview
A Python implementation of the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning algorithm using an LSTM network, tested on the CartPole environment from OpenAI Gym. It builds on a TensorFlow tutorial by Arthur Juliani and the 2016 paper by Mnih et al. The author states it is example code only and that this model does not converge on CartPole.
Key features
- A3C agent with LSTM layers, built with TensorFlow
- Trains only on minibatches larger than 30
- Uses a reward factor to allow faster learning rates
- Saves models every 100 episodes for reloading or testing
Best for
Readers who want to study how A3C with an LSTM is put together. The readme warns that it does not converge and points to a DDPG version for a working model.
- Upstream
- liampetti/A3C-LSTM
- Fork on GitHub
- Guo-astro/A3C-LSTM
- Upstream stars
- 48
- Category
- Finance, data and research
- Language
- Python
- License
- No license declaredWithout a license, the author keeps all rights. Ask the upstream owner before reusing the code.
- Forked
- 2018-05-20
- Sync status
- In syncLast synced 2026-09-29
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