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2018


Deep Reinforcement Learning for Event-Triggered Control
Deep Reinforcement Learning for Event-Triggered Control

Baumann, D., Zhu, J., Martius, G., Trimpe, S.

In Proceedings of the 57th IEEE International Conference on Decision and Control (CDC), pages: 943-950, 57th IEEE International Conference on Decision and Control (CDC), December 2018 (inproceedings)

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arXiv PDF DOI Project Page Project Page [BibTex]

2018


arXiv PDF DOI Project Page Project Page [BibTex]


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Discovering and Teaching Optimal Planning Strategies

Lieder, F., Callaway, F., Krueger, P. M., Das, P., Griffiths, T. L., Gul, S.

In The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018, Falk Lieder and Frederick Callaway contributed equally to this publication. (inproceedings)

Abstract
How should we think and decide, and how can we learn to make better decisions? To address these questions we formalize the discovery of cognitive strategies as a metacognitive reinforcement learning problem. This formulation leads to a computational method for deriving optimal cognitive strategies and a feedback mechanism for accelerating the process by which people learn how to make better decisions. As a proof of concept, we apply our approach to develop an intelligent system that teaches people optimal planning stratgies. Our training program combines a novel process-tracing paradigm that makes peoples latent planning strategies observable with an intelligent system that gives people feedback on how their planning strategy could be improved. The pedagogy of our intelligent tutor is based on the theory that people discover their cognitive strategies through metacognitive reinforcement learning. Concretely, the tutor’s feedback is designed to maximally accelerate people’s metacognitive reinforcement learning towards the optimal cognitive strategy. A series of four experiments confirmed that training with the cognitive tutor significantly improved people’s decision-making competency: Experiment 1 demonstrated that the cognitive tutor’s feedback accelerates participants’ metacognitive learning. Experiment 2 found that this training effect transfers to more difficult planning problems in more complex environments. Experiment 3 found that these transfer effects are retained for at least 24 hours after the training. Finally, Experiment 4 found that practicing with the cognitive tutor conveys additional benefits above and beyond verbal description of the optimal planning strategy. The results suggest that promoting metacognitive reinforcement learning with optimal feedback is a promising approach to improving the human mind.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Discovering Rational Heuristics for Risky Choice

Gul, S., Krueger, P. M., Callaway, F., Griffiths, T. L., Lieder, F.

The 14th biannual conference of the German Society for Cognitive Science, GK, The 14th biannual conference of the German Society for Cognitive Science, GK, September 2018 (conference)

Abstract
How should we think and decide to make the best possible use of our precious time and limited cognitive resources? And how do people’s cognitive strategies compare to this ideal? We study these questions in the domain of multi-alternative risky choice using the methodology of resource-rational analysis. To answer the first question, we leverage a new meta-level reinforcement learning algorithm to derive optimal heuristics for four different risky choice environments. We find that our method rediscovers two fast-and-frugal heuristics that people are known to use, namely Take-The-Best and choosing randomly, as resource-rational strategies for specific environments. Our method also discovered a novel heuristic that combines elements of Take-The-Best and Satisficing. To answer the second question, we use the Mouselab paradigm to measure how people’s decision strategies compare to the predictions of our resource-rational analysis. We found that our resource-rational analysis correctly predicted which strategies people use and under which conditions they use them. While people generally tend to make rational use of their limited resources overall, their strategy choices do not always fully exploit the structure of each decision problem. Overall, people’s decision operations were about 88% as resource-rational as they could possibly be. A formal model comparison confirmed that our resource-rational model explained people’s decision strategies significantly better than the Directed Cognition model of Gabaix et al. (2006). Our study is a proof-of-concept that optimal cognitive strategies can be automatically derived from the principle of resource-rationality. Our results suggest that resource-rational analysis is a promising approach for uncovering people’s cognitive strategies and revisiting the debate about human rationality with a more realistic normative standard.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


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Learning to Select Computations

Callaway, F., Gul, S., Krueger, P. M., Griffiths, T. L., Lieder, F.

In Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference, August 2018, Frederick Callaway and Sayan Gul and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
The efficient use of limited computational resources is an essential ingredient of intelligence. Selecting computations optimally according to rational metareasoning would achieve this, but this is computationally intractable. Inspired by psychology and neuroscience, we propose the first concrete and domain-general learning algorithm for approximating the optimal selection of computations: Bayesian metalevel policy search (BMPS). We derive this general, sample-efficient search algorithm for a computation-selecting metalevel policy based on the insight that the value of information lies between the myopic value of information and the value of perfect information. We evaluate BMPS on three increasingly difficult metareasoning problems: when to terminate computation, how to allocate computation between competing options, and planning. Across all three domains, BMPS achieved near-optimal performance and compared favorably to previously proposed metareasoning heuristics. Finally, we demonstrate the practical utility of BMPS in an emergency management scenario, even accounting for the overhead of metareasoning.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


A resource-rational analysis of human planning
A resource-rational analysis of human planning

Callaway, F., Lieder, F., Das, P., Gul, S., Krueger, P. M., Griffiths, T. L.

In Proceedings of the 40th Annual Conference of the Cognitive Science Society, May 2018, Frederick Callaway and Falk Lieder contributed equally to this publication. (inproceedings)

Abstract
People's cognitive strategies are jointly shaped by function and computational constraints. Resource-rational analysis leverages these constraints to derive rational models of people's cognitive strategies from the assumption that people make rational use of limited cognitive resources. We present a resource-rational analysis of planning and evaluate its predictions in a newly developed process tracing paradigm. In Experiment 1, we find that a resource-rational planning strategy predicts the process by which people plan more accurately than previous models of planning. Furthermore, in Experiment 2, we find that it also captures how people's planning strategies adapt to the structure of the environment. In addition, our approach allows us to quantify for the first time how close people's planning strategies are to being resource-rational and to characterize in which ways they conform to and deviate from optimal planning.

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DOI [BibTex]

DOI [BibTex]


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L4: Practical loss-based stepsize adaptation for deep learning

Rolinek, M., Martius, G.

In Advances in Neural Information Processing Systems 31 (NeurIPS 2018), pages: 6434-6444, (Editors: S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett), Curran Associates, Inc., 2018 (inproceedings)

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Github link (url) Project Page [BibTex]

Github link (url) Project Page [BibTex]


Systematic self-exploration of behaviors for robots in a dynamical systems framework
Systematic self-exploration of behaviors for robots in a dynamical systems framework

Pinneri, C., Martius, G.

In Proc. Artificial Life XI, pages: 319-326, MIT Press, Cambridge, MA, 2018 (inproceedings)

Abstract
One of the challenges of this century is to understand the neural mechanisms behind cognitive control and learning. Recent investigations propose biologically plausible synaptic mechanisms for self-organizing controllers, in the spirit of Hebbian learning. In particular, differential extrinsic plasticity (DEP) [Der and Martius, PNAS 2015], has proven to enable embodied agents to self-organize their individual sensorimotor development, and generate highly coordinated behaviors during their interaction with the environment. These behaviors are attractors of a dynamical system. In this paper, we use the DEP rule to generate attractors and we combine it with a “repelling potential” which allows the system to actively explore all its attractor behaviors in a systematic way. With a view to a self-determined exploration of goal-free behaviors, our framework enables switching between different motion patterns in an autonomous and sequential fashion. Our algorithm is able to recover all the attractor behaviors in a toy system and it is also effective in two simulated environments. A spherical robot discovers all its major rolling modes and a hexapod robot learns to locomote in 50 different ways in 30min.

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link (url) DOI Project Page [BibTex]

link (url) DOI Project Page [BibTex]


Learning equations for extrapolation and control
Learning equations for extrapolation and control

Sahoo, S. S., Lampert, C. H., Martius, G.

In Proc. 35th International Conference on Machine Learning, ICML 2018, Stockholm, Sweden, 2018, 80, pages: 4442-4450, http://proceedings.mlr.press/v80/sahoo18a/sahoo18a.pdf, (Editors: Dy, Jennifer and Krause, Andreas), PMLR, 2018 (inproceedings)

Abstract
We present an approach to identify concise equations from data using a shallow neural network approach. In contrast to ordinary black-box regression, this approach allows understanding functional relations and generalizing them from observed data to unseen parts of the parameter space. We show how to extend the class of learnable equations for a recently proposed equation learning network to include divisions, and we improve the learning and model selection strategy to be useful for challenging real-world data. For systems governed by analytical expressions, our method can in many cases identify the true underlying equation and extrapolate to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum system, where only 2 random rollouts are required to learn the forward dynamics and successfully achieve the swing-up task.

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Code Arxiv Poster Slides link (url) Project Page [BibTex]

Code Arxiv Poster Slides link (url) Project Page [BibTex]


Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns
Robust Affordable 3D Haptic Sensation via Learning Deformation Patterns

Sun, H., Martius, G.

Proceedings International Conference on Humanoid Robots, pages: 846-853, IEEE, New York, NY, USA, 2018 IEEE-RAS International Conference on Humanoid Robots, 2018, Oral Presentation (conference)

Abstract
Haptic sensation is an important modality for interacting with the real world. This paper proposes a general framework of inferring haptic forces on the surface of a 3D structure from internal deformations using a small number of physical sensors instead of employing dense sensor arrays. Using machine learning techniques, we optimize the sensor number and their placement and are able to obtain high-precision force inference for a robotic limb using as few as 9 sensors. For the optimal and sparse placement of the measurement units (strain gauges), we employ data-driven methods based on data obtained by finite element simulation. We compare data-driven approaches with model-based methods relying on geometric distance and information criteria such as Entropy and Mutual Information. We validate our approach on a modified limb of the “Poppy” robot [1] and obtain 8 mm localization precision.

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DOI Project Page [BibTex]

DOI Project Page [BibTex]

2013


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Controllability and Resource-Rational Planning

Lieder, F., Goodman, N. D., Huys, Q. J.

In Computational and Systems Neuroscience (Cosyne), pages: 112, 2013 (inproceedings)

Abstract
Learned helplessness experiments involving controllable vs. uncontrollable stressors have shown that the perceived ability to control events has profound consequences for decision making. Normative models of decision making, however, do not naturally incorporate knowledge about controllability, and previous approaches to incorporating it have led to solutions with biologically implausible computational demands [1,2]. Intuitively, controllability bounds the differential rewards for choosing one strategy over another, and therefore believing that the environment is uncontrollable should reduce one’s willingness to invest time and effort into choosing between options. Here, we offer a normative, resource-rational account of the role of controllability in trading mental effort for expected gain. In this view, the brain not only faces the task of solving Markov decision problems (MDPs), but it also has to optimally allocate its finite computational resources to solve them efficiently. This joint problem can itself be cast as a MDP [3], and its optimal solution respects computational constraints by design. We start with an analytic characterisation of the influence of controllability on the use of computational resources. We then replicate previous results on the effects of controllability on the differential value of exploration vs. exploitation, showing that these are also seen in a cognitively plausible regime of computational complexity. Third, we find that controllability makes computation valuable, so that it is worth investing more mental effort the higher the subjective controllability. Fourth, we show that in this model the perceived lack of control (helplessness) replicates empirical findings [4] whereby patients with major depressive disorder are less likely to repeat a choice that led to a reward, or to avoid a choice that led to a loss. Finally, the model makes empirically testable predictions about the relationship between reaction time and helplessness.

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[BibTex]

2013


[BibTex]


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Learned helplessness and generalization

Lieder, F., Goodman, N. D., Huys, Q. J. M.

In 35th Annual Conference of the Cognitive Science Society, 2013 (inproceedings)

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[BibTex]

[BibTex]


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Reverse-Engineering Resource-Efficient Algorithms

Lieder, F., Goodman, N. D., Griffiths, T. L.

In NIPS Workshop Resource-Efficient Machine Learning, 2013 (inproceedings)

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[BibTex]

[BibTex]


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Behavior as broken symmetry in embodied self-organizing robots

Der, R., Martius, G.

In Advances in Artificial Life, ECAL 2013, pages: 601-608, MIT Press, 2013 (incollection)

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[BibTex]

[BibTex]