Adaptive algorithms for shaping behavior
Fig 5
Algorithms for designing continuous curricula A: Decision tree showing the continuous version of ADP which includes actions that “grow” and “shrink” the increments between continuously parameterized difficulty levels.
See the text for more details of the task in the continuous setting. B: ADP significantly outperforms INC when the task is difficult (low ε). Barplot means are estimated from 10 repeats. C,D: The q values plotted as in Fig 3D, 3E. Similar to the discrete setting, INC shows catastrophic extinction and never learns the task for sufficiently small ε. Continuous ADP first decreases increment size and smoothly increases the difficulty level while balancing reinforcement and extinction.