This is am implementation of the Automated Residual Reinforcement Learning algorithm.
- python3.7
- tensorflow
- tf2rl
- pybullet
- gym
- wandb
- Basic Usage
python main.py --gait sine --policy TD3 --optimiser TBPSA
The gait
argument represents the gait pattern; it can be line/sine/rose/triangle.
The policy
argument determines the RL agent; it can be SAC/TD3
The optimiser
argument chooses the parameter optimisers; it can be BO/CMA/TBPSA
- More Arguments
state-mode
: This could change the state representation of the RL module.leg-action-mode
: This could change the action representation of the RL module.optimisation-mask
: This could change the parameter search space of the black-box optimiser.num-history-observation
: whether used stacked states as RL observation.
** Parts of this implementation are based on tf2rl. **