Abstract
We are motivated by the problem of comparing the complexity of one robotic task relative to another. To this end, we define a notion of reduction that formalizes the following intuition: Task 1 reduces to Task 2 if we can efficiently transform any policy that solves Task 2 into a policy that solves Task 1. We further define a quantitative measure of the relative complexity between any two tasks for a given robot. We prove useful properties of our notion of reduction (e.g., reflexivity, transitivity, and antisymmetry) and relative complexity measure (e.g., nonnegativity and monotonicity). In addition, we propose practical algorithms for estimating the relative complexity measure. We illustrate our framework for comparing robotic tasks using (i) examples where one can analytically establish reductions, and (ii) reinforcement learning examples where the proposed algorithm can estimate the relative complexity between tasks.
M. Ho and A. Farid—Equal contribution.
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References
Achille, A., Mbeng, G., Soatto, S.: Dynamics and reachability of learning tasks (2019). arXiv:1810.02440
Achille, A., Paolini, G., Mbeng, G., Soatto, S.: The information complexity of learning tasks, their structure and their distance. Inf. Inference: A J. IMA 10(1), 51–72 (2021)
Ahmadi, A.A., Majumdar, A., Tedrake, R.: Complexity of ten decision problems in continuous time dynamical systems. In: Proceedings of the American Control Conference (ACC), pp. 6376–6381 (2013)
Arora, S., Barak, B.: Computational Complexity: A Modern Approach. Cambridge University Press, New York, NY (2009)
Blondel, V., Tsitsiklis, J.: A survey of computational complexity results in systems and control. Automatica 36(9), 1249–1274 (2000)
Borie, R., Tovey, C., Koenig, S.: Algorithms and complexity results for pursuit-evasion problems. Int. Joint Conf. Artif. Intell. (IJCAI) 9, 59–66 (2009)
Borie, R., Tovey, C., Koenig, S.: Algorithms and complexity results for graph-based pursuit evasion. Auton. Robot. 31(4), 317–332 (2011)
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: Openai gym (2016). arXiv:1606.01540
Canny, J.: The Complexity of Robot Motion Planning. MIT Press, Cambridge, MA (1988)
Chang, M.B., Gupta, A., Levine, S., Griffiths, T.L.: Automatically composing representation transformations as a means for generalization. In: Proceedings of the International Conference on Learning Representations (2019)
Culberson, J.: Sokoban is PSPACE-complete. Technical Report TR 97-02, University of Alberta, Edmonton, Alberta, Canada (1997)
Donald, B.R.: On information invariants in robotics. Artif. Intell. 72(1), 217–304 (1995)
Erdmann, M.: Understanding action and sensing by designing action-based sensors. The International Journal of Robotics Research (IJRR) 14(5), 483–509 (1995)
Fudenberg, D., Drew, F., Levine, D.K.: The Theory of Learning in Games, vol. 2. MIT Press, Cambridge, MA (1998)
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems 27 (2014)
Haarnoja, T., Zhou, A., Abbeel, P., Levine, S.: Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In: Proceedings of the International Conference on Machine Learning, pp. 1861–1870 (2018)
Han, S., Stiffler, N., Krontiris, A., Bekris, K., Yu, J.: High-quality tabletop rearrangement with overhand grasps: hardness results and fast methods. In: Proceedings of Robotics: Science and Systems (RSS) (2017)
Hauser, K.: The minimum constraint removal problem with three robotics applications. Int. J. Robot. Res. (IJRR) 33(1), 5–17 (2014)
Hopcroft, J., Joseph, D., Whitesides, S.: Movement problems for 2-dimensional linkages. SIAM J. Comput. (SICOMP) 13(3), 610–629 (1984)
Hopcroft, J., Schwartz, J., Sharir, M.: On the complexity of motion planning for multiple independent objects; PSPACE-hardness of the Warehouseman’s Problem. Int. J. Robot. Res. (IJRR) 3(4), 76–88 (1984)
Joseph, D., Plantings, W.H.: On the complexity of reachability and motion planning questions. In: Proceedings of the Symposium on Computational Geometry, pp. 62–66 (1985)
LaValle, S.M.: Planning Algorithms. Cambridge University Press, Cambridge, MA (2006)
LaValle, S.M.: Sensing and Filtering: A Fresh Perspective Based on Preimages and Information Spaces. Publishers Inc., Hanover, MA (2012)
Li, M., Vitányi, P., et al.: An Introduction to Kolmogorov Complexity and Its Applications, vol. 3. Springer, New York, NY (2008)
Li, Y., Wu, Y., Xu, H., Wang, X., Wu, Y.: Solving compositional reinforcement learning problems via task reduction. In: Proceedings of the International Conference on Learning Representations (2021)
Murrieta-Cid, R., Monroy, R., Hutchinson, S., Laumond, J.P.: A Complexity result for the pursuit-evasion game of maintaining visibility of a moving evader. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 2657–2664 (2008)
O’Kane, J.M., LaValle, S.M.: Comparing the Power of Robots. Int. J. Robot. Res. (IJRR) 27(1), 5–23 (2008)
Reif, J.: Complexity of the mover’s problem and generalizations. In: Symposium on Foundations of Computer Science, pp. 421–427 (1979)
Saberifar, F.Z., Ghasemlou, S., Shell, D.A., O’Kane, J.M.: Toward a language-theoretic foundation for planning and filtering. Int. J. Robot. Res. (IJRR) 38(2–3), 236–259 (2019)
Shell, D.A., O’Kane, J.M.: Reality as a simulation of reality: robot illusions, fundamental limits, and a physical demonstration. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 10327–10334 (2020)
Sipser, M.: Introduction to the Theory of Computation. Cengage Learning, Boston, MA (2013)
Solovey, K., Halperin, D.: On the hardness of unlabeled multi-robot motion planning. Int. J. Robot. Res. (IJRR) 35(14), 1750–1759 (2016)
Todorov, E., Erez, T., Tassa, Y.: Mujoco: A physics engine for model-based control. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5026–5033 (2012)
Tran, A.T., Nguyen, C.V., Hassner, T.: Transferability and hardness of supervised classification tasks. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1395–1405 (2019)
Acknowledgements
The authors are grateful to the anonymous reviewers for their helpful feedback and suggestions on this work. Funding: NSF CAREER Award [#2044149] and Office of Naval Research [N00014-21-1-2803, N00014-18-1-2873].
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Appendices
Appendix
A Proof of Properties
Proposition
1(Task Reduction is a Non-Strict Partial Ordering Relation). Suppose that \(\forall \ (\tau _\xi , \tau _\zeta ) \in \mathcal {T}^2\), \(H_{\xi , \zeta }\) and \(G_{\zeta , \xi }\) include the identity and are closed under composition on \(\mathcal {T}\). Then, task reductions satisfy the following properties and thus define a non-strict partial ordering relation.
Property
1.a. Reflexivity: \(\tau _1 \preceq \tau _1\).
Property
1.b. Antisymmetry: \(\tau _1 \prec \tau _2 \implies \lnot (\tau _2 \preceq \tau _1)\), where \(\tau _1 \prec \tau _2\) is defined as \((\tau _1 \preceq \tau _2) \wedge \lnot (\tau _1 \equiv \tau _2)\).
Property
1.c. Transitivity: \((\tau _1 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _3) \implies \tau _1 \preceq \tau _3\).
Proof
Property 1.a: \(\tau _1 \preceq \tau _1 \implies \exists \ g \in G_{1,1}\), \(h \in H_{1,1}\) such that
If g and h are the identity function, then \(g \circ \pi ^\star _1 \circ h = \pi ^\star _1 \ \forall \ \pi ^\star _1 \in \varPi ^\star _1\). Thus, \(\tau _1 \preceq \tau _1\) when \(G_{1,1}\) and \(H_{1,1}\) include their respective identity functions.
Property 1.b: Suppose \(\tau _1 \prec \tau _2\) and thus \((\tau _1 \preceq \tau _2) \wedge \lnot (\tau _1 \equiv \tau _2)\). Note that \(\lnot (\tau _1 \equiv \tau _2) \implies \lnot \big ((\tau _1 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _1) \big ) \implies \lnot (\tau _1 \preceq \tau _2) \vee \lnot (\tau _2 \preceq \tau _1)\). We assumed \((\tau _1 \preceq \tau _2)\), so we must have that \(\lnot (\tau _2 \preceq \tau _1)\).
Property 1.c: Suppose \(\tau _1 \preceq \tau _2\) and \(\tau _2 \preceq \tau _3\). By Definition 4, \(\exists \ g_1 \in G_{2,1}\), \(g_2 \in G_{3,2}\), \(h_1 \in H_{1,2}\), and \(h_2 \in H_{2,3}\) such that \(g_1 \circ \pi ^\star _2 \circ h_1 \in \varPi ^\star _1 \ \forall \ \pi ^\star _2 \in \varPi ^\star _2\) and \(g_2 \circ \pi ^\star _3 \circ h_2 \in \varPi ^\star _2 \ \forall \ \pi ^\star _3 \in \varPi ^\star _3\). Consider
for all \(\pi ^\star _3 \in \varPi ^\star _3\). Let \(g_3 := g_1 \circ g_2\) and \(h_3 := h_2 \circ h_1\) so that \(g_3 \circ \pi ^\star _3 \circ h_3 \in \varPi ^\star _1\) for all \(\pi ^\star _3 \in \varPi ^\star _3\). If \(G_{3,1}\) and \(H_{1,3}\) are closed under composition on \(\mathcal {T}\), then \(g_3 \in G_{3,1}\) and \(h_3 \in H_{1,3}\) and \(\tau _1 \preceq \tau _3\). Thus, task reductions are transitive if \(G_{3,1}\) and \(H_{1,3}\) are closed under composition on \(\mathcal {T}\). \(\square \)
Proposition 6
(Strict Task Reduction is a Strict Partial Ordering Relation). Suppose that \(\forall \ (\tau _\xi , \tau _\zeta ) \in \mathcal {T}^2\), \(H_{\xi , \zeta }\) and \(G_{\zeta , \xi }\) include the identity and are closed under composition on \(\mathcal {T}\). Then, strict task reductions satisfy the following properties and thus define a strict partial ordering relation.
Property
6.a. Irreflexivity: \(\lnot (\tau _1 \prec \tau _1)\).
Property
6.b. Asymmetry: \(\tau _1 \prec \tau _2 \implies \lnot (\tau _2 \prec \tau _1)\).
Property
6.c. Transitivity: \(\tau _1 \prec \tau _2 \wedge \tau _2 \prec \tau _3 \implies \tau _1 \prec \tau _3\).
Proof
Property 6.a: Suppose \(\tau _1 \prec \tau _1 \implies \tau _1 \preceq \tau _1 \wedge \lnot (\tau _1 \equiv \tau _1)\). \(\tau _1 \equiv \tau _1\) by Property 2.b. \(\Rightarrow \!\Leftarrow \implies \lnot (\tau _1 \prec \tau _1)\) when \(H_{1,1}\) and \(G_{1,1}\) include their respective identity functions.
Property 6.b: \(\tau _1 \prec \tau _2 \implies \lnot (\tau _2 \preceq \tau _1)\) by Property 1.b, since \(\tau _1 \prec \tau _2 \implies \lnot (\tau _1 \equiv \tau _2)\). \(\lnot (\tau _2 \preceq \tau _1) \iff \lnot (\tau _2 \preceq \tau _1) \vee \tau _2 \equiv \tau _1 \implies \lnot \big (\tau _2 \preceq \tau _1 \wedge \lnot (\tau _2 \equiv \tau _1)\big ) \implies \lnot (\tau _2 \prec \tau _1).\)
Property 6.c: \(\tau _1 \prec \tau _2 \wedge \tau _2 \prec \tau _3 \implies \tau _1 \preceq \tau _2 \wedge \tau _2 \preceq \tau _3 \wedge \lnot (\tau _1 \equiv \tau _2) \wedge \lnot (\tau _2 \equiv \tau _3) \implies \tau _1 \preceq \tau _3 \wedge \lnot (\tau _1 \equiv \tau _3)\) by Properties 1.c and 2.c. \(\implies \tau _1 \prec \tau _3\) when \(H_{1,3}\) and \(G_{3,1}\) are closed under composition on \(\mathcal {T}\). \(\square \)
Proposition
2(Task Equivalence is an Equivalence Relation). Suppose that \(\forall \ (\tau _\xi , \tau _\zeta ) \in \mathcal {T}^2\), \(H_{\xi , \zeta }\) and \(G_{\zeta , \xi }\) include the identity and are closed under composition on \(\mathcal {T}\). Then, task equivalence satisfies the following properties and thus defines an equivalence relation.
Property
2.a. Reflexivity: \(\tau _1 \equiv \tau _1\).
Property
2.b. Symmetry: \(\tau _1 \equiv \tau _2 \implies \tau _2 \equiv \tau _1\).
Property
2.c. Transitivity: \(\tau _1 \equiv \tau _2 \wedge \tau _2 \equiv \tau _3 \implies \tau _1 \equiv \tau _3\).
Proof
Property 2.a: \(\tau _1 \equiv \tau _1 \implies \tau _1 \preceq \tau _1\) by Property 1.a when \(G_{1,1}\) and \(H_{1,1}\) include the identity. Thus, task equivalence is reflexive if \(G_{1,1}\) and \(H_{1,1}\) include the identity.
Property 2.b: \(\tau _1 \equiv \tau _2 \implies (\tau _1 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _1)\) by Definition 5 \(\implies (\tau _2 \preceq \tau _1) \wedge (\tau _1 \preceq \tau _2)\) \(\implies \tau _2 \equiv \tau _1\).
Property 2.c: \((\tau _1 \equiv \tau _2) \wedge (\tau _2 \equiv \tau _3) \implies (\tau _1 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _3) \wedge (\tau _3 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _1)\) by Definition 5. \((\tau _3 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _1) \implies (\tau _3 \preceq \tau _1)\) by Property 1.c when \(G_{3,1}\) and \(H_{1,3}\) are closed under composition on \(\mathcal {T}\). Similarly, \((\tau _1 \preceq \tau _2) \wedge (\tau _2 \preceq \tau _3) \implies (\tau _1 \preceq \tau _3)\). Thus \((\tau _1 \preceq \tau _3) \wedge (\tau _3 \preceq \tau _1) \implies \tau _1 \equiv \tau _3\). Thus task equivalence is transitive if \(G_{3,1}\) and \(H_{1,3}\) are closed under composition on \(\mathcal {T}\). \(\square \)
Proposition
5(Properties of the Relative Complexity). Relative Complexity satisfies the following properties:
Property
5.a. Nonnegativity and boundedness: \(C_{\tau _1 / \tau _2} \in [0,1]\).
Property
5.b. Monotonicity with respect to H and G: If \(H \subseteq H'\) and \(G \subseteq G'\), then \(C_{\tau _1 / \tau _2}(H', G') \preceq C_{\tau _1 / \tau _2}(H, G)\).
Assume that the supremum and infimum in Definition 7 are attained by functions in \(\varPi ^\star _2, H, G\). Then:
Property
5.c. Equivalence between reduction and 0 relative complexity: \(C_{\tau _1 / \tau _2} = 0 \iff \tau _1 \preceq \tau _2\).
Property
5.d. Equivalence between no reduction and positive relative complexity: \(C_{\tau _1 / \tau _2} \in (0,1] \iff \lnot (\tau _1 \preceq \tau _2)\).
Proof
Property 5.a: \(R_1(g \circ \pi ^\star _2 \circ h) \in [0, R^\star _1]\). Therefore, \(R_1(g \circ \pi ^\star _2 \circ h) / R^\star _1 \in [0, 1] \implies C_{\tau _1/\tau _2} \in [0, 1]\) for any H, G.
Property 5.b: Consider \(H, H'\) such that \(H \subseteq H'\) and \(G, G'\) such that \(G \subseteq G'\). For any function f, the following is true \(\forall \pi _2^\star \):
This implies the following:
Property 5.c: Assume \(C_{\tau _1 / \tau _2} = 0\) for some H and G \(\iff \) for any \(\pi ^\star _2 \in \varPi ^\star _2 \ \exists \ g\in G\) and \(h \in H\) such that \(R_1(g\circ \pi ^\star _2 \circ h) = R^\star _1\). \(R_1(\pi _1) = R^\star _1\) \(\iff \) \(\pi _1 \in \varPi ^\star _1\). Thus, for all \(\pi ^\star _2 \in \varPi ^\star _2 \ \exists \ g \in G\) and \(h \in H\) such that \(g\circ \pi ^\star _2 \circ h \in \varPi ^\star _1\) \(\iff \tau _1 \preceq \tau _2\).
Property 5.d: The contrapositive of Property 5.c is \(\lnot (\tau _1 \preceq \tau _2) \iff C_{\tau _1 / \tau _2} \ne 0 \). By Property 5.a, the complexity measure is \(C_{\tau _1 / \tau _2} \in [0,1]\), therefore, \(C_{\tau _1 / \tau _2} \in (0,1] \iff C_{\tau _1 / \tau _2} \ne 0\). Thus \(C_{\tau _1 / \tau _2} \in (0,1] \iff \lnot (\tau _1 \preceq \tau _2)\). \(\square \)
B Additional Experimental Details
Approximating Relative Complexity using Q-learning. We apply Q-learning to Algorithm 1 by letting the loss functions \(L_1\) and \(L_2\) correspond to a Q-learning loss: \(L_\xi (\pi _\xi ) = -\frac{1}{B}\sum _{b=1}^{B}[Q^{\pi _\xi }(s_b,a_b) \log p(a_b)]\), where \(p(a_b)\) corresponds to the probability of an action for policy \(\pi _\xi \) (which may be a transformation of another policy such as \(\pi _\xi = g \circ \pi _{\zeta } \circ h\)), \(Q^{\pi _\xi }(s_b,a_b)\) are the Q-values, and B is the batch size. We run Algorithm 1 for 1000 iterations and use a batch size B of 1000 transitions.
Approximating Relative Complexity using SAC. We modify Algorithm 1 to use SAC for approximating the relative complexity. Let \(Q_2^{\pi _2}\) be a critic of \(\pi _2\) on task \(\tau _2\) and \(Q_1^{g \circ \pi _2 \circ h}\) be a critic of \(g \circ \pi _2 \circ h\) on task \(\tau _1\). We add an additional step to the algorithm for updating the critics on task \(\tau _1\) and \(\tau _2\). The critics are then used in the updates for the policy \(\pi _2\) and the encoder/decoder. The resulting method is presented in Algorithm 2. We run Algorithm 2 for 50,000 iterations and use a batch size of 200 transitions.
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Ho, M., Farid, A., Majumdar, A. (2023). Towards a Framework for Comparing the Complexity of Robotic Tasks. In: LaValle, S.M., O’Kane, J.M., Otte, M., Sadigh, D., Tokekar, P. (eds) Algorithmic Foundations of Robotics XV. WAFR 2022. Springer Proceedings in Advanced Robotics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-031-21090-7_17
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