Deep Exploration 6.5 Crack

[Sara Ruballos]

Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as epsilon-greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not compatible with nonlinearly parameterized value functions. As a first step towards addressing such contexts we develop bootstrapped DQN. We demonstrate that bootstrapped DQN can combine deep exploration with deep neural networks for exponentially faster learning than any dithering strategy. In the Arcade Learning Environment bootstrapped DQN substantially improves learning speed and cumulative performance across most games.


High-throughput sequencing (HTS) has revolutionized science by enabling super-fast detection of genomic variants at base-pair resolution. Consequently, it poses the challenging problem of identification of technical artifacts, i.e. hidden non-random error patterns. Understanding the properties of sequencing artifacts holds the key in separating true variants from false positives. Here, we develop Mapinsights, a toolkit that performs quality control (QC) analysis of sequence alignment files, capable of detecting outliers based on sequencing artifacts of HTS data at a deeper resolution compared with existing methods. Mapinsights performs a cluster analysis based on novel and existing QC features derived from the sequence alignment for outlier detection. We applied Mapinsights on community standard open-source datasets and identified various quality issues including technical errors related to sequencing cycles, sequencing chemistry, sequencing libraries and across various orthogonal sequencing platforms. Mapinsights also enables identification of anomalies related to sequencing depth. A logistic regression-based model built on the features of Mapinsights shows high accuracy in detecting 'low-confidence' variant sites. Quantitative estimates and probabilistic arguments provided by Mapinsights can be utilized in identifying errors, bias and outlier samples, and also aid in improving the authenticity of variant calls.



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Platinum-based chemotherapy is usually curative for patients with testicular germ cell tumors (TGCT), but a subset of patients experience disease progression and poor clinical outcomes. Here, we tested whether immune profiling of TGCT could identify novel prognostic markers and therapeutic targets for this patient cohort. We obtained primary and metastatic TGCT samples from one center. We performed immune profiling using multiplexed fluorescence immunohistochemistry (FIHC) for T-cell subsets and immune checkpoints, and targeted gene expression profiling (Nanostring nCounter Immune panel). Publically available data sets were used to validate primary sample analyses. Nearly all samples had some degree of T-cell infiltration and immune checkpoint expression. Seminomas were associated with increased CD3+ T-cell infiltration, decreased Regulatory T-cells, increased PD-L1, and increased PD-1/PD-L1 spatial interaction compared with non-seminomas using FIHC. Gene expression profiling confirmed these findings and also demonstrated increased expression of T-cell markers (e.g., IFNγ, and LAG3) and cancer/testis antigens (e.g., PRAME) in seminomas, whereas non-seminomas demonstrated high neutrophil and macrophage gene signatures. Irrespective of histology, advanced TGCT stage was associated with decreased T-cell and NK-cell signatures, while Treg, neutrophil, mast cell and macrophage signatures increased with advanced stage. Importantly, cancer/testis antigen, neutrophil, and CD8+/regulatory T-cell signatures correlated with recurrence free survival. Thus, deep immune characterization of TGCT using IHC and gene expression profiling identified activated T-cell infiltration which correlated with seminoma histology and good prognosis. These results may provide a rationale for testing of anti-PD-1/PD-L1 agents and suggest prognostic markers.


While a large number of algorithms for optimizing quantum dynamics for different objectives have been developed, a common limitation is the reliance on good initial guesses, being either random or based on heuristics and intuitions. Here we implement a tabula rasa deep quantum exploration version of the Deepmind AlphaZero algorithm for systematically averting this limitation. AlphaZero employs a deep neural network in conjunction with deep lookahead in a guided tree search, which allows for predictive hidden-variable approximation of the quantum parameter landscape. To emphasize transferability, we apply and benchmark the algorithm on three classes of control problems using only a single common set of algorithmic hyperparameters. AlphaZero achieves substantial improvements in both the quality and quantity of good solution clusters compared to earlier methods. It is able to spontaneously learn unexpected hidden structure and global symmetry in the solutions, going beyond even human heuristics.


Mounting evidence has shown that imposing significant constraints in the dynamics may lead to such complexity,11,24,25,26 especially as QOCT has veered into high-precision quantum computation,27 circuit compilation,28 and architecture design.29 It is therefore crucial to balance resources for exploitation of smooth, local quantum landscapes with state-of-the-art classical methods for domain-agnostic exploration.


In the literature, optimization of dynamically evolving systems is characterized by a lookahead-depth, i.e. how far into the future one plans current actions. A shallow depth may broaden exploration, a strategy typically found in Reinforcement Learning (RL).30 This has been powerfully combined with Deep Neural Networks (DNN)31,32,33,34,35 and applied recently to quantum systems.36,37,38,39,40,41,42,43 Unfortunately, single-step lookaheads are inherently local and thus require a slower learning rate, with no performance gain found over full-depth, domain-specialized (Hessian approximation) methods in QOCT. Other full-depth methods have also had mixed success, e.g. Genetic Algorithms44,45 and Differential Evolution,25 but they typically require careful fine-tuning since they are based on ad hoc heuristics rather than being mathematically rooted.


A recent stunning breakthrough has been due to the AlphaZero class of algorithms.46,47,48 AlphaZero has already effectively outclassed all adversaries in the games of Go, Chess, Shogu, and Starcraft. The key to the success of AlphaZero was the combination of a Monte Carlo tree search with a one-step lookahead DNN. As a result, the lookahead information from far down the tree dramatically increases the trained DNN precision, and together they compound to produce much more focused and heuristic-free exploration.


Here, we implement and benchmark a QOCT version of AlphaZero for optimizing quantum dynamics. We characterize improvements in learning and exploration compared to traditional methods. We find a crossover from difficult problems where AlphaZero learning alone is ideal and those where a combination of deep exploration and quantum-specialized exploitation is optimal. We show this leads to a dramatic increase in both the quality and quantity of good solution clusters. Our AlphaZero implementation retains the tabula rasa character of ref. 47 in two important respects. Firstly, it efficiently learns to solve three different optimization problem classes using the same algorithmic hyperparameters. Secondly, we demonstrate that AlphaZero is able to identify quantum-specific heuristics in the form of hidden symmetries without the need for expert knowledge.






In what follows we apply the algorithm with a unified set of algorithmic parameters (hyperparameters) to three optimization classes: discrete, continuous, and continous with strong constraints. We have found these hyperparameters by fine-tuning them with respect to the continuous problem. The three problem types accentuate different optimization strategies. In the discrete optimization case, we show how AlphaZero stands up against other domain-agnostic methods (where the gradient is not defined) and compare their abilities to learn structures in the parameters. For the constrained continuous pulses, we validate the hypothesis that the analytical gradient, while computable, is highly inefficient and indeed unable to find near global solutions that are at least as good as those found by AlphaZero. Finally, in the continuous-valued piecewise-constant case, we show the balance between state-of-the-art physics-specialized and agnostic AlphaZero approaches. We show that the combination of exploration and exploitation is able to produce new clusters of high-quality solutions that are otherwise highly unlikely to be found, while learning hidden problem symmetry.


AlphaZero and GA are both learning algorithms in the sense that they utilize previous obtained solutions in order to form new ones. We compare the learning curves for the two algorithms in Fig. 2c, where we have plotted the infidelity as a function of wall time at 60 ns. For AlphaZero, we use the infidelity after each episode, where each data point is unique. For GA, we use the best performing solution in the population after each iteration. Since GA is a relatively greedy algorithm it performs very well initially, but fails to explore the larger solution space as the members in the population converge upon a single class of solution and the learning curve flattens out. In contrast, AlphaZero keeps a high level of exploration that ultimately allows it to reach a very large number of different high-fidelity solutions.


So far, we have considered problems where gradient searches have not been applicable (digital sequence) or where gradient searches become inefficient (constrained analog pulses). For specific tasks where highly specialized algorithms exist and are known to perform relatively well, domain-agnostic algorithms typically perform inadequately. Thus, to properly benchmark our algorithm we have also considered the domain of piecewise-constant pulses, a scenario where GRAPE typically performs extremely well due to the presence of high-frequency components and the limited number of matrix multiplications. In the following we hence focus on picewise constant pulses where we choose a single-step duration of 2 ns. In this scenario, we characterize the performance of the exploitation and exploration algorithms in terms of both the variety of solutions found and the quality of the solutions. Here we also introduce a new RL algorithm, named Q-learning. Q-learning was one of the first RL algorithms developed, and applied recently to quantum control.24,37 It is a tabular-based algorithm that applies one-step updates in order to solve the optimal Bellman equation61 (see Methods). Here we apply an extended version that uses eligibility traces and regular replays, similar to ref. 37 Originally, we attempted to apply Q-learning to digital gates (SFQ) pulses, but ultimately the algorithm failed due to the very large search space, where tabular-based methods are known to break down. This is one reason why modern RL algorithms use deep neural networks instead, motivating also our use of AlphaZero.

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