RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
(a) Objectworld – original \mathbf{s}^{(t)} ’s; (b) Objectworld – re-optimized \mathbf{s}^{(t)} ’s; (c) Highway – original \mathbf{s}^{(t)} ’s; (d) Highway – re-optimized \mathbf{s}^{(t)} ’s; Reverse transfer. Difference in error in the learned reward between when a task was first trained and after the full model had been trained, as a function of task order. Positive change in errors indicates positive transfer; negative change indicates interference from negative transfer. Note that the re-optimization has both decreased negative transfer on the earliest tasks, and also significantly increased the magnitude of positive reverse transfer. Red curves show the best exponential curve.
Figure 4 : Results for extensions of ELIRL. Whiskers denote standard errors. (a) Reward difference (lower is better) between MaxEnt, in-domain ELIRL, and cross-domain ELIRL. Transferring knowledge across domains improved the accuracy of the learned reward. (b) Value difference (lower is better) obtained by ELIRL and AME-IRL on the planar navigation environment. ELIRL improves the performance of AME-IRL, and this improvement increases as ELIRL observes more tasks.; Results for extensions of ELIRL. Whiskers denote standard errors. (a) Reward difference (lower is better) between MaxEnt, in-domain ELIRL, and cross-domain ELIRL. Transferring knowledge across domains improved the accuracy of the learned reward. (b) Value difference (lower is better) obtained by ELIRL and AME-IRL on the planar navigation environment. ELIRL improves the performance of AME-IRL, and this improvement increases as ELIRL observes more tasks.
The average learning time per task. The standard error is reported after the \pm .