Reinforcement Learning (RL) models

Overview

RL agents that can perform any dynamic foraging task in aind-behavior-gym and can fit behavior using MLE.

Code structure

• To add more generative models, please subclass DynamicForagingAgentMLEBase.

Implemented foragers

• ForagerQLearning: Simple Q-learning agents that incrementally update Q-values.

  Available agent_kwargs:

  number_of_learning_rate: Literal[1, 2] = 2,
  number_of_forget_rate: Literal[0, 1] = 1,
  choice_kernel: Literal["none", "one_step", "full"] = "none",
  action_selection: Literal["softmax", "epsilon-greedy"] = "softmax",
  

• ForagerLossCounting: Loss counting agents with probabilistic loss_count_threshold.

  Available agent_kwargs:

  win_stay_lose_switch: Literal[False, True] = False,
  choice_kernel: Literal["none", "one_step", "full"] = "none",
  

Here is the full list of available foragers:

Usage

• Jupyter notebook

• See also these unittest functions.

RL model playground

Play with the generative models here.