The main features of this release are: 1) JAX support. 2) Multi-agent training. 3) Comprehensive documentation with new structure and theme.

This unexpected new version has focused on supporting the training and evaluation of reinforcement learning algorithms in NVIDIA Isaac Orbit. Added: a) Isaac Orbit environment loader, b) Wrap an Isaac Orbit environment, c) Gaussian-Deterministic shared model instantiator

[skrl-0.9.0] - 2023-01-13

Added - Support for Farama Gymnasium interface - Wrapper for robosuite environments - Weights & Biases integration (by @juhannc) - Set the running mode (training or evaluation) of the agents - Allow clipping the gradient norm for DDPG, TD3 and SAC agents - Initialize model biases - Add RNN (RNN, LSTM, GRU and any other variant) support for A2C, DDPG, PPO, SAC, TD3 and TRPO agents - Allow disabling training/evaluation progressbar - Farama Shimmy and robosuite examples - KUKA LBR iiwa real-world example

Changed - Forward model inputs as a Python dictionary [*breaking change*] - Returns a Python dictionary with extra output values in model calls [*breaking change*] - Adopt the implementation of `terminated` and `truncated` over `done` for all environments

Fixed - Omniverse Isaac Gym simulation speed for the Franka Emika real-world example - Call agents' method `record_transition` instead of parent method to allow storing samples in memories during evaluation - Move TRPO policy optimization out of the value optimization loop - Access to the categorical model distribution - Call reset only once for Gym/Gymnasium vectorized environments

Removed - Deprecated method `start` in trainers