Benchmarking Perturbation-based Saliency Maps for Explaining Deep Reinforcement Learning Agents

Recent years saw a plethora of work on explaining complex intelligent agents. One example is the development of several algorithms that generate saliency maps which show how much each pixel attributed to the agents' decision. However, most evaluations of such saliency maps focus on image classification tasks. As far as we know, there is no work which thoroughly compares different saliency maps for Deep Reinforcement Learning agents. This paper compares four perturbation-based approaches to create saliency maps for Deep Reinforcement Learning agents trained on four different Atari 2600 games. All four approaches work by perturbing parts of the input and measuring how much this affects the agent's output. The approaches are compared using three computational metrics: dependence on the learned parameters of the agent (sanity checks), faithfulness to the agent's reasoning (input degradation), and run-time.Comment: Presented on the Explainable Agency in Artificial Intelligence Workshop during the 35th AAAI Conference on Artificial Intelligenc

Myotonic Dystrophy-A Progeroid Disease?

Myotonic dystrophies (DM) are slowly progressing multisystemic disorders caused by repeat expansions in the DMPK or CNBP genes. The multisystemic involvement in DM patients often reflects the appearance of accelerated aging. This is partly due to visible features such as cataracts, muscle weakness, and frontal baldness, but there are also less obvious features like cardiac arrhythmia, diabetes or hypogammaglobulinemia. These aging features suggest the hypothesis that DM could be a segmental progeroid disease. To identify the molecular cause of this characteristic appearance of accelerated aging we compare clinical features of DM to "typical" segmental progeroid disorders caused by mutations in DNA repair or nuclear envelope proteins. Furthermore, we characterize if this premature aging effect is also reflected on the cellular level in DM and investigate overlaps with "classical" progeroid disorders. To investigate the molecular similarities at the cellular level we use primary DM and control cell lines. This analysis reveals many similarities to progeroid syndromes linked to the nuclear envelope. Our comparison on both clinical and molecular levels argues for qualification of DM as a segmental progeroid disorder

Nuclear Envelope Transmembrane Proteins in Myotonic Dystrophy Type 1

Myotonic dystrophy type 1 (DM1) is a multisystemic disorder with predominant myotonia and muscular dystrophy which is caused by CTG-repeat expansions in the DMPK gene. These repeat expansions are transcribed and the resulting mRNA accumulates RNA-binding proteins involved in splicing, resulting in a general splicing defect. We observed nuclear envelope (NE) alterations in DM1 primary myoblasts. These included invaginations of the NE as well as an altered composition of the nuclear lamina. Specifically, we investigated NE transmembrane proteins (NETs) in DM1 primary myoblasts, staining to determine if their distribution was altered compared to controls and if this could contribute to these structural defects. We also tested the expression of these NETs in muscle and how localization changes in the DM1 primary myoblasts undergoing differentiation in vitro to myotubes. We found no changes in the localization of the tested NETs, but most tended to exhibit reduced expression with increasing DMPK-repeat length. Nonetheless, the DM1 patient expression range was within the expression range of the controls. Additionally, we found a down-regulation of the possible nesprin 1 giant isoform in DM1 primary myoblasts which could contribute to the increased NE invaginations. Thus, nesprin 1 may be an interesting target for further investigation in DM1 disease pathology

Implementation and comparison of occlusion-based explainable Artifcial Intelligence methods

In this Bachelor thesis, four different occlusion-based explainable AI approaches are implemented and compared

Implementation and comparison of occlusion-based explainable Artifcial Intelligence methods

In this Bachelor thesis, four different occlusion-based explainable AI approaches are implemented and compared

Implementation and comparison of occlusion-based explainable Artifcial Intelligence methods

In this Bachelor thesis, four different occlusion-based explainable AI approaches are implemented and compared

Benchmarking perturbation-based saliency maps for explaining Atari agents

One of the most prominent methods for explaining the behavior of Deep Reinforcement Learning (DRL) agents is the generation of saliency maps that show how much each pixel attributed to the agents' decision. However, there is no work that computationally evaluates and compares the fidelity of different perturbation-based saliency map approaches specifically for DRL agents. It is particularly challenging to computationally evaluate saliency maps for DRL agents since their decisions are part of an overarching policy, which includes long-term decision making. For instance, the output neurons of value-based DRL algorithms encode both the value of the current state as well as the expected future reward after doing each action in this state. This ambiguity should be considered when evaluating saliency maps for such agents. In this paper, we compare five popular perturbation-based approaches to create saliency maps for DRL agents trained on four different Atari 2,600 games. The approaches are compared using two computational metrics: dependence on the learned parameters of the underlying deep Q-network of the agents (sanity checks) and fidelity to the agents' reasoning (input degradation). During the sanity checks, we found that a popular noise-based saliency map approach for DRL agents shows little dependence on the parameters of the output layer. We demonstrate that this can be fixed by tweaking the algorithm such that it focuses on specific actions instead of the general entropy within the output values. For fidelity, we identify two main factors that influence which saliency map approach should be chosen in which situation. Particular to value-based DRL agents, we show that analyzing the agents' choice of action requires different saliency map approaches than analyzing the agents' state value estimation