toolbox

Homer supports custom services, and provides a template to easily add new ones.

It could be great to add some more. Before starting to work on a custom service please open an issue to describe your idea and discuss about it.

Some ide

The following applies to DDPG and TD3, and possibly other models. The following libraries were installed in a virtual environment:

numpy==1.16.4
stable-baselines==2.10.0
gym==0.14.0
tensorflow==1.14.0

Episode rewards do not seem to be updated in model.learn() before callback.on_step(). Depending on which callback.locals variable is used, this means that:

• episode rewards may n

🐛 Bug

The documentation of DQN agent (https://stable-baselines3.readthedocs.io/en/master/modules/dqn.html) specifies that log_interval parameter is "The number of timesteps before logging". However, when set to 1 (or any other value) the logging is not made at that pace but is instead made every log_interval episode (and not timesteps). In the example below this is made every 200 timesteps.

As the title says, this would be a parameter that allows the quality measurement parameters to be computed against a known image, rather than change per iteration.

Something like:

[img, qual] = SIRT(proj,geo,angles, 'QualMeas','RMSE','ground_truth',my_image)