Buffers
Basis
This class is the abstraction for all buffers. In short, each buffer should support adding new samples and sampling from the buffer. Additional classes are required for saving or resuming the whole state. All buffers internally store data in as Experience but on sampling these are converted into torch Tensors or numpy arrays.
- class ai_traineree.types.experience.Experience(**kwargs)
Basic data unit to hold information.
It typically represents a one whole cycle of observation - action - reward. Data type used to store experiences in experience buffers.
Replay Buffer
The most basic buffer. Supports uniform sampling.