Rl-toolkit

Features 🔊
- Gymnasium
- DeepMind Control Suite wrapper
- ELU activation
- Optional state-action merging layer index (Critic model)
Bug fixes 🛠️
- Optimized critic
- Optimized server
- `backend.epsilon()` from Keras backend

Bug fixes 🛠️
- update default `config.yaml`

Features 🔊
- .fit()
- AgentCallback

Features 🔊
- Render environments to WanDB
- Grouping of runs in WanDB
- SampleToInsertRatio rate limiter
- Global Gradient Clipping to avoid exploding gradients
- Softplus for numerical stability
- YAML configuration file
- LogCosh instead of Huber loss
- Critic network with Add layer applied on state & action branches
- Custom uniform initializer
- XLA (Accelerated Linear Algebra) compiler
- Optimized Replay Buffer (https://github.com/deepmind/reverb/issues/90)
- split into **Agent**, **Learner**, **Tester** and **Server**
Bug fixes 🛠️
- Fixed creating of saving path for models
- Fixed model's `summary()`

Features 🔊
- Reverb
- `setup.py` (package is available on PyPI)
- split into **Agent**, **Learner** and **Tester**
- Use custom model and layer for defining Actor-Critic
- MultiCritic - concatenating multiple critic networks into one network
- Truncated Quantile Critics

Features 🔊
- update Dockerfile
- update `README.md`
- formatted code by Black & Flake8