Adaptive algorithms for shaping behavior
Fig 4
Deep reinforcement learning agents trained using a curriculum solve navigation tasks with delayed rewards.
A: The trail tracking paradigm. A sample trajectory of a trained agent navigating a randomly sampled odor trail. The colors show odor concentration. The inset shows the egocentric visuospatial input received by the network, where the agent’s location is in red and odor detections are in green. B: Sample trails from the six difficulty levels. C: ADP outperforms INC and RAND (each teacher-student interaction is a step). The agent does not learn the task without a curriculum. Results are plotted from 5 repeats. D: The success rate of the agent in finding the target over training (black dashed line) for INC and ADP. The curriculum is shown in red. Note the significant forgetting shown by the student trained using INC approach compared to ADP. E-G: As in panels A-D for a localization task. The agent is required to navigate towards a source which emits Poisson-distributed cues whose detection probability decreases with distance from the source (colored in green on a log scale). Results are plotted from 15 repeats.