32,994 research outputs found
Michael Rodriguez interviews fiction writer Michael Kimball
No full textAuthor Michael Kimball talks about moving away from Michigan to become a successful writer, his education, the fiction reading series he has started in Baltimore, the life-story-on-postcard project, and his book "Dear everybody." Kimball is interviewed by Michigan State University Librarian Michael Rodriguez for the Michigan State University Libraries' Michigan Writers Series
Michael Rodriguez interviews author Paul Clemens
No full textAuthor Paul Clemens talks about his book "Made in Detroit," the genre of memoir, and writing about race. Clemens is interviewed by Michigan State University Librarian Michael Rodriguez for the MSU Libraries' Michigan Writers Series. Held in the MSU Main Library
Michael Rodriguez interviews author Tom Springer
No full textAuthor Tom Springer is interviewed about his writing career and his newest book "Looking for hickories". Springer talks about his career following after earning an Environmental Journalism degree from Michigan State University. He calls his genre "creative non-fiction" and explains how he weaves his memories into his books about life in rural and wild Michigan. Part of the Michigan State University Libraries' Michigan Writers Series. Springer is interviewed by Librarian Michael Rodriguez
Michael Rodriguez interviews author Gary Gildner
No full textAuthor Gary Gildner explains why he left his tenured teaching position to move to Idaho to became a full-time writer of poetry. Gildner talks about donating his personal papers to Michigan State University Libraries' Special Collections, his writing style and how he approaches writing. Gildner is interviewed by MSU Librarian Michael Rodriguez for the MSU Libraries' Michigan Writer Series. Held at the MSU Main Library
Gold standard of UK degrees is lost in translation
No full textInflated marks, overworked staff and politically compromised courses are the price of exploiting offshore UK registered students, says Michael Day
Optimal control theoretic value function learning
No full textGenerating behaviours to complete complex tasks can be viewed under the paradigm of controlling dynamical systems. To solve such tasks, most approaches fall under two paradigms: Reinforcement Learning (RL) and Optimal Control (OC) theoretic approaches. OC theoretic solutions are mostly local and can only provide global controllers for special cases. As a result of this, local solutions become trajectories. Synthesising these trajectories in the deterministic setting is formalised under the calculus of variations. This paradigm imposes strict constraints on the objective landscape: differentiability and continuity. In the stochastic setting, OC theoretic solutions have been proposed to remove the burden of these constraints and infer the optimal trajectory through sampling. RL has very similar theoretical groundings but diverges significantly in its approach. For example, RL parametersises value and/or policy functions instead of trajectories, allowing generalisation to new initial conditions. Additionally, in its model-free setting, which is our focus in this thesis, there is no need for constraints such as differentiability on the objective. RL is capable of estimating the gradients via sampling. However, these gradient estimates come at the high price of noisy solutions and slow convergence. To this end, defining methods that can leverage the best of both approaches is desirable. Our thesis aims to derive methods that greatly remove the burden of cost function design on the user while enabling generalisation by efficiently learning approximate global controllers.
As our initial attempt at this formalisation, we introduce a local method that combines the efficiency of derivative-based OC-theoretic approaches with the flexibility of local solutions based on sampling. To this end, we propose a hybrid approach that aims for consensus between the derivative-based solution of iterative Linear Quadratic Regulator (iLQR) and the sampling-based solution of Path Integral (PI) control. We define an objective that enables us to sample when derivatives vanish and follow optimised trajectories when derivatives arise. We use the Kullback Leibler (KL) control interpretation of PI control to formulate an inference problem that computes the optimal controls constrained by an adaptive distribution defined by the solution of iLQR. Our results show better convergence on manipulation and obstacle avoidance tasks than sampling strategy, path integral control and gradient-based strategy iLQR.
In the second segment of this thesis, we evaluate the widely used RL algorithms and its core gradient estimation machinery, policy gradients, without the typical convergence strategies. Our results are obtained on simple nonlinear continuous control problems. We show that RL still requires extensive tuning, even on simple nonlinear problems and the flexibility gained by zeroth-order derivative estimation is paid for by hyperparameter tuning. In turn, we propose an OC-theoretic approach based on Bellman optimality that leverages differentiable dynamics and first-order gradients. Our approach can learn approximate time-varying value functions and robustly converge with minimal tuning. We further verify the ability of our method by relaxing the objective and obtaining first-order approximations of time-varying Lyapunov constraints. We further verify our approach by satisfying this first-order constraint over a compact set of initial conditions. When comparing our method to Soft Actor-Critic (SAC) and Proximal Policy Optimisation (PPO) we show faster convergence and outperform PPO and SAC in task cost by at least 2 and 4 orders of magnitude, respectively.
In the third part of the thesis, we combine our findings from the previous sections to create a method that can handle discontinuities using stochasticity, ensure convergence with differentiability, and generalise with function parameterisation. To achieve this, we approach the problem using stochastic optimal control and robustness. We use the stochastic Hamilton-Jacobi-Bellman equation, differentiable dynamics, and the natural smoothing induced by stochastic first-order gradients. Our results demonstrate that the policies based on learned value functions outperform SAC and PPO in task cost by factors of up to 1076.02 and 8, respectively. Moreover, we observe that adding noise to the dynamics smoothens the curvature of the value function. This effect is especially noticeable in our obstacle navigation task with discontinuous dynamics and costs, where the value functions learned under noisier dynamics follow wider paths around obstacles, making them more robust. Finally, we show that our learned value functions can also be integrated into local methods, reducing their effective search horizon by a factor of 15
Michael Rodriguez interviews historian and author Keith Widder
No full textHistorian and author Keith Widder talks about his move to Michigan from Wisconsin, his career as Curator of History for the Mackinac Island State Park Commission, his research interests, his book "Michigan Agricultural College", and his current projects. Widder is interviewed by Michigan State University Librarian Michael Rodriguez for the MSU Libraries' Michigan Writers Series. Held in the MSU Main Library
Dr. Michael Janis, Morehouse College, August 2011, August 2011
No full textThis video is a conversation with Dr. Michael Janis. Dr. Janis talks about his book, "Africa After Modernism: Transitions in Literature, Media and Philosophy". Yolanda Gilmore-Bivins, AUC Woodruff Library, is the interviewer
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
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