Naive independent optimization via gradient descent is prone to get stuck in local optima. Merel et al. Third, read slightly older but seminal papers (one or two years old) with many citations. the Deadly Triad), something anyone who has toyed around with DQNs will have experienced. The algorithm did not ‘fully’ learn end-to-end what the right sequence of moves is to solve a cube & then do the dexterous manipulation required. Vinyals, O., I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, et al. the most outer pixels of an ATARI frame) which was rarely relevant to success. Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. All in all 2019 has highlighted the immense potential of Deep RL in previously unimagined dimensions. This work had also been awarded the ‘best paper’ award. This constrains the agent to learn one thing at a time while parallel learning of individual contexts would be beneficial. These are only a few of the accepted papers and it is obvious that the researchers from Google, Microsoft, MIT, Berkeley are one of the top contributors and collaborators for many works. Schrittwieser, J., I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, et al. Z. Leibo, and N. De Freitas, Baker, B., I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch, Schaul, T., D. Borsa, J. Modayil, and R. Pascanu, Galashov, A., S. M. Jayakumar, L. Hasenclever, D. Tirumala, J. Schwarz, G. Desjardins, W. M. Czarnecki, Y. W. Teh, R. Pascanu, and N. Heess, Merel, J., L. Hasenclever, A. Galashov, A. Ahuja, V. Pham, G. Wayne, Y. W. Teh, and N. Heess, Lowe, R., Y. Wu, A. Tamar, J. Harb, O. The concept of influence is thereby grounded in a counterfactual assessment: How would another agent’s action change if I had acted differently in this situation. Now this is one amazing paper! Their experiments show that this is able to distill 2707 experts & perform effective one-shot transfer resulting in smooth behaviors. PlaNet 2.0; Hafner et al., 2019). Dreamer learns by propagating “analytical” gradients of learned state values through imagined trajectories of a world model. Instead I tried to distill some key narratives as well as stories that excite me. This paper proposes to add an inductive bias by ordering the neurons (ON), which ensures that when a given neuron is updated, all the neurons that follow it in the ordering are also updated. “And the first place in the category ‘Large-Scale DRL Projects’ goes to…” (insert awkward opening of an envelope with a microphone in one hand) + : DeepMind’s AlphaStar project led by Oriol Vinyals. Source: Deep Learning on Medium #ODSC – Open Data ScienceApr 23We’re just about finished with Q1 of 2019, and the research side of deep learning technology is forging ahead at a … Learning an Animatable Detailed 3D Face Model from In-The-Wild Images. Given a current history and a small look-ahead snippet, the model has to predict the action that enables such a transition (aka an inverse model). The results in this study show that recurrent architecture, ordered neurons LSTM (ON-LSTM), achieves good performance on language modelling, unsupervised parsing, targeted syntactic evaluation, and logical inference. - DeepMind’s AlphaStar (Vinyals et al, 2019). I’ve tried to include both links to the original papers and their code where possible. Please feel free to pull requests or open an issue to add papers… In this article, we will focus on the 5 papers that left a really big impact on us in this year. They then log the Jacobian at every action-state pair and optimize a pertubation objective which resembles a form of denoising autoencoder. Our day to day life is filled with situations which require anticipation & Theory of Mind. Machine learning, especially its subfield of Deep Learning, had many amazing advances in the recent years, and important research papers may lead to breakthroughs in technology that get used by billio ns of people. The action can thereby be thought of as a bottleneck between a future trajectory and a past latent state. ... We’re just about finished with Q1 of 2019, and the research side of deep learning technology is forging ahead at a very good clip. Importantly, the expert policies are not arbitrary pre-trained RL agents, but 2 second snippets of motion capture data. That is impressive. Given such a powerful ‘motor primitive’ embedding, one still has to obtain the student policy given the expert rollouts. Best Paper Awards. But it is human made & purposed to increase our quality of life. The International Conference on Learning Representations (ICLR) is one of the highly regarded deep learning conferences conducted every year at the end of spring. The empirical validation is performed on contextual bandits. Date: Tuesday, Sept 17, 2019, 11:00-12:30 Location: Auditorium Chair: Giovanni Semeraro The expert demonstrations are used to pre-train the policy of the agent via supervised minimization of a KL objective & provide an efficient regularization to ensure that the exploration behavior of the agent is not drowned by StarCraft’s curse of dimensionality. It has already made a huge impact in areas, such as cancer diagnosis, precision medicine, self-driving cars, predictive forecasting, and speech recognition. The problem is reduced to a regression which predicts rewards, values & policies & the learning of a representation function $h_\theta$ which maps an observation to an abstract space, a dynamics function $g_\theta$ as well as a policy and value predictor $f_\theta$. They don’t only significantly stabilize learning but also allow for larger learning rates & bigger epochs. The agents undergo 6 distinct phases of dominant strategies where shifts are based on the interaction with tools in the environment. ICLR considers a variety of topics for the conference, such as: Here are few works (in no particular order) presented at the recently concluded ICLR conference at New Orleans, US, which make an attempt at pushing the envelope of deep learning to newer boundaries: Usually, Long short-term memory (LSTM) architectures allow different neurons to track information at different time scales but they do not have an explicit bias towards modelling a hierarchy of constituents. To help you quickly get up to speed on the latest ML trends, we’re introducing our research series, […] This can lead to significant instabilities (e.g. I tried to choose the winners for the first category based on the scientific contributions and not only the massive scaling of already existing algorithms. The year 2019 saw an increase in the number of submissions. deep learning 2019 IEEE PAPERS AND PROJECTS FREE TO DOWNLOAD . Joint learning induces a form of non-stationarity in the environment which is the core challenge of Multi-Agent RL (MARL). Finally, they get rid of centralized access to other agents policies by having agents learn to predict each others behavior, a soft-version of Theory of Mind. Good thing that there are people working on increasing the sample (but not necessarily computational) efficiency via hallucinating in a latent space. UPDATE: We’ve also summarized the top 2020 AI & machine learning research papers. I don’t want to know the electricity bill, OpenAI & DeepMind have to pay. The open source machine learning and artificial intelligence project, neon is best for the senior or expert machine learning developers. Best Deep Learning Research of 2019 So Far. AI conferences like NeurIPS, ICML, ICLR, ACL and MLDS, among others, attract scores of interesting papers every year. Also, I am personally especially excited about how this might relate to evolutionary methods such as Population-Based Training (PBT). & Geoffrey H. (2015) (Cited: 5,716) Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. The 2019 edition witnessed over fifteen hundred submissions of which 524 papers were accepted. NeurIPS 2019was the 33rd edition of the conference, held between 8th and 14th December in Vancouver, Canada. PlaNet 2.0; Hafner et al., 2019), Social Influence as Intrinsic Motivation (Jaques et al., 2019), Autocurricula & Emergent Tool-Use (OpenAI, 2019), Non-Staggered Meta-Learner’s Dynamics (Rabinowitz, 2019), Information Asymmetry in KL-Regularized RL (Galashov et al., 2019), NPMP: Neural Probabilistic Motor Primitives (Merel et al., 2019), Grandmaster level in StarCraft II using multi-agent reinforcement learning, Mastering ATARI, Go, Chess and Shogi by planning with a learned model, Dream to Control: Learning Behaviors by Latent Imagination, Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning, Reward Shaping for Decentralized Training, Emergent tool use from multi-agent autocurricula, Environment Curriculum Learning for Multi-Agent Setups, Meta-learners’ learning dynamics are unlike learners’, Empirical characterization of Meta-Learner’s inner loop dynamics, Ray Interference: a Source of Plateaus in Deep Reinforcement Learning, Analytical derivation of plateau phenomenon in on-policy RL, Information asymmetry in KL-regularized RL, Neural probabilistic motor primitives for humanoid control. of skills and the path is caused by a coupling of learning and data generation arising due to on-policy rollouts, hence an interference. Finally, a few interesting observations regarding large-scale implementation: Learning dynamics in Deep RL remain far from being understood. Strictly speaking this work by OpenAI may not be considered a pure MARL paper. Still there have been some major theoretical breakthroughs revolving around new discoveries (such as Neural Tangent Kernels). Domain Randomization has been proposed to obtain a robust policy. If you couldn’t make it to CVPR 2019, no worries. [Related Article: The Most Influential Deep Learning Research of 2019] A survey on intrinsic motivation in reinforcement learning. By automatically increasing/decreasing the range of possible environment configurations based on the learning progress of the agent, ADR provides a pseudo-natural curriculum for the agent. We constantly assume the reaction of other individuals and readjust our beliefs based on recent evidence. Instead of training the agent on a single environment with a single set of environment-generating hyperparameters, the agent is trained on a plethora of different configurations. Time that is costly & could otherwise be used to generate more (but noisy) transitions in environment. The GitHub URL is here: neon. Finally, it might help us design learning signals which allow for fast adaptation. I have a master's degree in Robotics and I write…. But this is definitely not all there is. Few-shot learning has been regarded as the crux of intelligence. A total of 774 papers got accepted for ICML 2019 out of 3424 initial submissions (22.6% acceptance rate). 7 Dec 2020 • YadiraF/DECA • . I am excited for what there is to come in 2020 & believe that it is an awesome time to be in the field. Best machine learning paper award: Aniket Pramanik and colleagues from the University of Iowa, USA for the paper “Off-The-Grid Model Based Deep Learning (O-MoDL)”. So this is my personal top 10 - let me know if I missed your favorite paper! Check out the full list of accepted papers here. Hafner, D., T. Lillicrap, J. Ba, and M. Norouzi, Jaques, N., A. Lazaridou, E. Hughes, C. Gulcehre, P. Ortega, D. Strouse, J. Here is an infographic showing top contributors. The authors show that this can be circumvented by learning a default policy which constrains the action spaces & thereby reduces the complexity of the exploration problem. This was an observation already made in the MA-DDPG paper by. 2018 was a busy year for deep learning based Natural Language Processing (NLP) research. Akkaya, I., M. Andrychowicz, M. Chociej, M. Litwin, B. McGrew, A. Petron, A. Paino, et al. However, there is no comparable benchmark for cooperative multi-agent RL. Chatbots are … 2019 - What a year for Deep Reinforcement Learning (DRL) research - but also my first year as a PhD student in the field. 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