Added - A2C agent - Isaac Gym (preview 4) environment loader - Wrap an Isaac Gym (preview 4) environment - Support for OpenAI Gym vectorized environments - Running standard scaler for input preprocessing - Installation from PyPI (`pip install skrl`)
0.6.0
Added - Omniverse Isaac Gym environment loader - Wrap an Omniverse Isaac Gym environment - Save best models during training
0.5.0
Added - TRPO agent - Wrapper for DeepMind environments - KL Adaptive learning rate scheduler - Handle `gym.spaces.Dict` observation spaces (OpenAI Gym and DeepMind environments) - Forward environment info to agent `record_transition` method - Expose and document the random seeding mechanism - Define rewards shaping function in agents' config - Define learning rate scheduler in agents' config - Improve agent's algorithm description in documentation (PPO and TRPO at the moment)
Changed - Compute the Generalized Advantage Estimation (GAE) in agent `_update` method - Move noises definition to `resources` folder - Update the Isaac Gym examples
Removed - `compute_functions` for computing the GAE from memory base class
0.4.1
Added - Examples of all Isaac Gym environments (preview 3) - TensorBoard file iterator for data post-processing
Fixed - Init and evaluate agents in ParallelTrainer
0.4.0
Added - CEM, SARSA and Q-learning agents - Tabular model - Parallel training using multiprocessing - Isaac Gym utilities
Changed - Initialize agents in a separate method - Change the name of the `networks` argument to `models`
Fixed - Reset environments after post-processing
0.3.0
Added - DQN and DDQN agents - Export memory to files - Postprocessing utility to iterate over memory files - Model instantiator utility to allow fast development - More examples and contents in the documentation
Fixed - Clip actions using the whole space's limits