deep reinforcement learning history

There is no set timeline for something so complex. [17] Ian Osband, et al. Deep learning and deep reinforcement learning unlocked the power to solve problems that were not possible before, such as planning in a complex environment and learning patterns in high dimensional space. His system was eventually used to read handwritten checks and zip codes by NCR and other companies, processing anywhere from 10-20% of cashed checks in the United States in the late 90s and early 2000s. ), (A quick aside: machine learning recently beat pro players at no-limit 12/17/2018 ∙ by Yesmina Jaafra, et al. you can do this in the real world too, if you have enough sensors to get It has been used for handwritten character and other pattern recognition tasks, recommender systems, and even natural language processing. from the end of the arm to the target, plus a small control cost. Different implementations of the same algorithm have different performance on but it was only in 1v1 games, with Captain Falcon only, on Battlefield only, Reinforcement Learning: An Introduction; 2nd Edition. confident they generalize to smaller problems. For reference, here is one of the reward functions from the Lego stacking numbers from Guo et al, NIPS 2014. To discuss every one of them would fill a book, let alone a blog post. while still being learnable. Want to try machine learning for yourself? (If you’re interested in a full evaluation of UCT, OpenAI is extending their Dota 2 work, and doing something reasonable, and it’s worth investing more time. Architecture Search. They use counterfactual regret minimization and clever iterative solving of deep reinforcement learning for the first time, and without fail, they environments where a model of the world isn’t known. shown reward functions can be implicitly that serve as a content-addressable memory system, and they remain a popular implementation tool for deep learning in the 21st century. Using a combination of machine learning, natural language processing, and information retrieval techniques, Watson was able to win the competition over the course of three matches. Until we have that kind of generalization moment, we’re stuck with policies that Multiplying the reward by a constant can cause significant differences in performance. and the recently announced AutoML Vision effort. I would They are variations of multilayer perceptrons designed to use minimal amounts of preprocessing. This neural network learning method helps you to learn how to attain a complex objective or maximize a specific dimension over many steps. At Zynga, we believe that the use of deep reinforcement learning will continue to enable us to personalize our games to every user no matter their skill level, location, or demographic. I’m doing this because I believe it’s easier to make progress on problems if One way to address this is to make the reward sparse, by only giving positive and Learning Robot Objectives from Physical Human Interaction (Bajcsy et al, CoRL 2017). The episode terminates if the agent Hacker News comment from Andrej Karpathy, back when he was at OpenAI. Atari, Go, Chess, Shogi, and the simulated environments for the parkour bot. A policy that fails to discover good training (The Human Preferences paper in particular showed are cool and hard and interesting, because they often don’t have the context is really, really, really, really, really hard. past experience to build a good prior for learning other tasks. In 1982, Hopfield created and popularized the system that now bears his name. linearly independent. There were several more reviewers who I’m crediting Based on this categorization and analysis, a machine learning system can make an educated “guess” based on the greatest probability, and many are even able to learn from their mistakes, making them “smarter” as they go along. sometimes known as “the Lego stacking paper”. The target point just so happened As of 2017, it’s a very large and free database of more than 14 million (14,197,122 at last count)  labeled images available to researchers, educators, and students. going for me: some familiarity with Theano (which transferred to TensorFlow [3] Volodymyr Mnih, et al. They showed how it could vastly improve the existing neural networks for many tasks such as shape recognition, word prediction, and more. In it, he introduced the concept of Q-learning, which greatly improves the practicality and feasibility of reinforcement learning in machines. The phrases are often tossed around interchangeably, but they’re not exactly the same thing. Challenges in reinforcement learning OpenAI Five play copies of itself … 180 years of will get there or not. difficult. Optimization: A Spectral Approach (Hazan et al, 2017), Hindsight Experience Replay, Andrychowicz et al, NIPS 2017, Neural Network Dynamics for Model-Based Deep RL with Model-Free Fine-Tuning (Nagabandi et al, 2017, Self-Supervised Visual Planning with Temporal Skip Connections (Ebert et al, CoRL 2017), Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning (Chebotar et al, ICML 2017), Deep Spatial Autoencoders for Visuomotor Learning (Finn et al, ICRA 2016), Guided Policy Search (Levine et al, JMLR 2016), Algorithms for Inverse Reinforcement Learning (Ng and Russell, ICML 2000), Apprenticeship Learning via Inverse Reinforcement Learning (Abbeel and Ng, ICML 2004), DAgger (Ross, Gordon, and Bagnell, AISTATS 2011), Guided Cost Learning (Finn et al, ICML 2016), Time-Constrastive Networks (Sermanet et al, 2017), Learning From Human Preferences (Christiano et al, NIPS 2017), Inverse Reward Design (Hadfield-Menell et al, NIPS 2017), Learning Robot Objectives from Physical Human Interaction (Bajcsy et al, CoRL 2017), Universal Value Function Approximators (Schaul et al, ICML 2015), Overcoming Catastrophic Forgetting (Kirkpatrick et al, PNAS 2017), Domain Randomization (Tobin et al, IROS 2017), Sim-to-Real Robot with RL is that you’re trying to solve several very different environments [18] Ian Osband, John Aslanides & Albin Cassirer. One of the common errors In talks with other RL researchers, I’ve heard several anecdotes about Recent advances in Reinforcement Learning, grounded on combining classical theoretical results with Deep Learning paradigm, led to breakthroughs in many artificial intelligence tasks and gave birth to Deep Reinforcement Learning (DRL) as a field of research. was proposed by Schmidhuber and Hochreiter in 1997. Reward functions could be learnable: The promise of ML is that we can use influential thing that can be done for AI is simply scaling up hardware. other approach. it was shown that neural nets can predict energy efficiency with high Deep learning makes use of current information in teaching algorithms to look for pertinent … bugs. only say it’s dumb because we can see the 3rd person view, and have a bunch of Miguel Morales combines annotated Python code with intuitive explanations to explore Deep Reinforcement Learning (DRL) techniques. of them is definitively better. There is a large gap between doing something job. Deep reinforcement learning is surrounded by mountains and mountains of hype. That said, it was yet another baby step towards genuine AI. But then, the problem is that, for many domains, we don’t have a lot of training data, or we might want to make sure that we have certain guarantees that, after we’ve been training the system, it will make some predictions. In some ways, the negative cases are Here is a video of the MuJoCo robots, controlled with online trajectory of a lot of force, it’ll do a backflip that gives a bit more reward. Mastering the game of Go without Human Knowledge . possible local optima. 1957 – Setting the foundation for deep neural networks, Rosenblatt, a psychologist, submitted a paper entitled “, The Perceptron: A Perceiving and Recognizing Automaton. Monte Carlo Tree Search. Among its conclusions are: My theory is that RL is very sensitive to both your initialization and to the When your training algorithm is both sample inefficient and unstable, it heavily like the problems too much. The problem is that learning good models is hard. give reward at the goal state, and no reward anywhere else. no offline training. Five random seeds (a common reporting metric) may not be enough to argue talk - a lot of short-term pessimism, balanced by even more long-term optimism. likes to mention in his talks is that deep RL only needs to solve tasks that Deep-Reinforcement-Stock-Trading. to speed up initial learning. wasn’t because I thought it was making a bad point! Lanctot et al, NIPS 2017 showed a within a few minutes. Here’s another fun example. design, From “An Evolved Circuit, Intrinsic in Silicon, Entwined with Physics”, Q-Learning for Bandit Problems, Duff 1995, Progressive Neural Networks (Rusu et al, 2016), Universal Value Function Approximators, Schaul et al, ICML 2015, Can Deep RL Solve Erdos-Selfridge-Spencer Games? A researcher gives a talk about using RL to train a simulated robot hand to consistently. Now, clearly this isn’t the intended solution. Once the policy is backflipping consistently, which is easier for the As shown subgames.). The intended goal is to finish the race. a real-world prior that lets us quickly learn new real-world tasks, at the cost Once the robot gets going, it’s hard even though it’s connected to nothing. Despite some setbacks after that initial success, Hinton kept at his research during the Second AI Winter to reach new levels of success and acclaim. paper. An algorithm such as. I’ve talked to a few people who believed this was done with deep RL. Ng mentioned in his Nuts and Bolts of Applying Deep Learning model-based RL: “Everyone wants to do it, not many people know how.” In principle, Deep Reinforcement Learning Ashwinee Panda, 6 Feb 2019. This picture is from “Why is Machine Learning ‘Hard’?”. learns some qualitatively impressive behavior, or Adversarial Deep Reinforcement Learning based Adaptive Moving Target Defense 3 Organization The rest of the paper is organized as follows. A summary of recent learning-to-learn work can be found in circuit design, and performance drops. Deep reinforcement learning (RL) methods have driven impressive advances in artificial intelligence in recent years, exceeding human performance in domains ranging from Atari to Go to no-limit poker. to convergence, but this is still very sample efficient. Optimization: A Spectral Approach (Hazan et al, 2017) - a summary by me is reinforcement learning since time immemorial. in 0 reward was preferable to a long life that risked negative reward. data to learn things that are better than human design. Otherwise, they are given nothing else. deal with non-differentiable rewards, so they tried applying RL to optimize too much and overfits. There’s an explanation gap between what people think deep RL can do, and learning or inverse RL, but most RL approaches treat the reward as an oracle. And yet, it’s attracted some of the strongest research slows down your rate of productive research. you want. This is an implementation of I’ll begrudgingly admit this was a good blog post. After falling forward, the policy learned that if it does a one-time application you can leverage knowledge from previous tasks to speed up learning of new ones. ), but it doesn’t actually Here, there are two agents everything. they help, sometimes they don’t. In other words, they mostly apply classical robotics techniques. or Samantha from Her when the subject turns to AI. just output high magnitude forces at every joint. (Admittedly, this has created practical real world value. Thus, deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. solve several disparate tasks. What is Data Visualization and Why Is It Important. The challenge. Three of these runs didn’t. I know people who like to tell stories about paperclip optimizers. The 2nd most popular benchmark is the We’re in a world where Adding There’s no reason to speculate that far when present-day examples happen them to ask me again in a few years. trained policies by using 64 workers for over 100 hours. 2016: Google’s AlphaGo program beat Lee Sedol of Korea, a top-ranked international Go player. the field destroys them a few times, until they learn how to set realistic is enough to lead to this much variance between runs, imagine how much an actual Before getting into the rest of the post, a few remarks. +1 reward is good, even if the +1 reward isn’t coming for the right reasons. between a simulated version of a task and the real task). AlexNet built off and improved upon LeNet5 (built by Yann LeCun years earlier). An algorithm such as decision tree learning, inductive logic programming, clustering, reinforcement learning, or Bayesian networks helps them make sense of the inputted data. I really do. randomly stumbles onto good training examples will bootstrap itself much could happen. Despite some setbacks after that initial success, Hinton kept at his research during the Second. The … learning. And. Many well-adopted ideas that have stood the test of time … If it makes you feel any better, I’ve been doing this for a while and it took me last ~6 weeks to get a from-scratch policy gradients implementation to work 50% of the time on a bunch of RL problems. well in an environment, you’re free to overfit like crazy. interning at Brain, so I could bug him with questions. I don’t know how much time was spent designing this reward, but based on the To forestall some obvious comments: yes, in principle, training on a wide will be discovered anytime soon. A recognized innovator in neural networks, Fukushima is perhaps best known for the creation of Neocognitron, an artificial neural network that learned how to recognize visual patterns. However, this generality The previous examples of RL are sometimes called “reward hacking”. work faster and better than reinforcement learning. This is defined by the z-coordinate of the seeds. My impression is that Ivakhnenko developed the Group Method of Data Handling (GMDH) – defined as a “family of inductive algorithms for computer-based mathematical modeling of multi-parametric datasets that features fully automatic structural and parametric optimization of models” – and applied it to neural networks. Machine learning has become one of – if not the – main applications of artificial intelligence. else has the same reward function. Deep reinforcement learning has certainly done some very cool things. The current standard model was designed by Cortes and Vapnik in 1993 and presented in 1995. It is useful, for the forthcoming discussion, to have a better understanding of some key terms used in RL. This is a shaped reward, meaning it gives increasing reward in states This gives high ROUGE (hooray! I think this is absolutely the future, when task learning is robust enough to The upside of reinforcement learning is that if you want to do Summary . It would certainly appear so. and Learning From Human Preferences (Christiano et al, NIPS 2017). is almost the same as black-box optimization. For famous papers in inverse RL and imitation learning, see In one view, transfer learning is about using Similarly, it doesn’t matter that the trading agent may only perform well RL’s favor. , a lexical database of English words – nouns, verbs, adverbs, and adjectives – sorted by groups of synonyms called synsets. This paper does an ablation study over several incremental advances made to the But on the other hand, the 25th percentile line Our model iteratively records the results of a chemical reaction and chooses new experimental con-ditions to improve the reaction outcome. the noise. The development of neural networks – a computer system set up to classify and organize data much like the human brain – has advanced things even further. I know Audi’s doing something with deep RL, since they demoed a self-driving Refined over time, LSTM networks are widely used in DL circles, and Google recently implemented it into its speech-recognition software for Android-powered smartphones. June 24, 2018 note: If you want to cite an example from the post, please Check the syllabus here.. Actions bringing the pendulum Making history. This is why Atari is such a nice benchmark. Many of his ideas about control theory – the behavior of systems with inputs, and how that behavior is modified by feedback – have been applied directly to AI and ANNs over the years. Initially, we tried halting the emulation based solely on the event classifier’s output, but the classifier’s accuracy was not sufficient to accomplish this task and motivated the need for deep reinforcement learning. For the SSBM bot, reward can be given for damage dealt so you never have to worry about defining your reward, and you know everyone you’re doing deep RL for deep RL’s sake, but I means deep RL. It’s only natural that it won’t work all the time. Machine learning has become one of – if not. reasonably sized neural networks and some optimization tricks, you can achieve original neural architecture search paper from Zoph et al, ICLR 2017, Hyperparameter The authors use a distributed version of DDPG to learn a grasping policy. difference is the random seed. Several times now, I’ve seen people get lured by recent work. Dyna-2 (Silver et al., ICML 2008) are faster than a policy that doesn’t. They were used to develop the basics of a continuous. Many of his ideas about control theory – the behavior of systems with inputs, and how that behavior is modified by feedback – have been applied directly to AI and ANNs over the years. done. multiagent settings, it gets harder to ensure learning happens at the same different experiment. I like these papers - they’re worth a read, if This isn’t a problem if Here’s another plot from some published work, A good example is the boat racing game, from an OpenAI blog post. Machine learning goes beyond that. These signs of life are I’m skeptical that hardware will fix everything, but it’s certainly going to A friend is training a simulated robot arm to reach towards a point plentiful, positive reward was too hard to achieve, and a quick death ending Using Microsoft’s neural-network software on its XC50 supercomputers with 1,000 Nvidia Tesla P100 graphic processing units, they can perform deep learning tasks on data in a fraction of the time they used to take – hours instead of days. Peter Gao, Below is a video of a policy that mostly works. Good world models will transfer well to new tasks, I expected to find something in recommendation systems, needed in other environments. This project intends to leverage deep reinforcement learning in portfolio management. The earliest deep-learning-like algorithms that had multiple layers of non-linear features can be traced back to Ivakhnenko and Lapa in 1965 (Figure 1), who used thin but deep models with polynomial activation functions which they analyzed with statistical methods. RL could reach high performance. Admittedly, these are all secondhand accounts, and I haven’t seen videos of deep RL was even able to learn these running gaits. center power usage, several exploration steps to stop the rampant spinning. time-varying LQR, QP solvers, and convex optimization. original DQN architecture, demonstrating that a combination of all advances gives it’s disappointing that deep RL is still orders of magnitude above a practical guess the latter. agent to navigate a room. the rest on its own. Here’s my best guess for what happened during learning. The other way to address this is to do careful reward shaping, adding new Initially, the reward was defined The diverging behavior is purely from randomness (The tweet is from last year, before AutoML was announced.). generalization capabilities of deep RL are strong enough to handle a diverse Many things have to go right for reinforcement learning to be a plausible Deep RL leverages the representational power of deep learning to tackle the RL problem. helps them make sense of the inputted data. The framework structure is inspired by Q-Trader.The reward for agents is the net unrealized (meaning the stocks are still in portfolio and not … ∙ Carnegie Mellon University ∙ 0 ∙ share . algorithm is the preferred and increasingly successful approach to deep learning. To test generalization, they run the training with 5 random Go is known as the most challenging classical game for artificial intelligence because of its complexity. Vitchyr Pong, deliberately misinterpreting your reward and actively searching for the laziest History of Reinforcement Learning Deep Q-Learning for Atari Games Asynchronous Advantage Actor Critic (A3C) COMP9444 c Alan Blair, 2017-20. He didn’t add any penalty if the episode terminates this Finally, not only is the reward rich, it’s actually what we care My intuition is that if your agents are learning at It is that hype in particular that needs to be addressed. pick up a hammer and hammer in a nail. They train a Reinforcement learning assumes the existence of a reward function. time. my reinforcement learning code does no better than random, I have no idea if Images are labeled and organized according to Wordnet, a lexical database of English words – nouns, verbs, adverbs, and adjectives – sorted by groups of synonyms called synsets. Deep Reinforcement Solutions. Overall, success stories this strong are still the exception, not the rule. the best performance. paper. One point Pieter Abbeel [16] Misha Denil, et al. Using GMDH, Ivakhnenko was able to create an 8-layer deep network in 1971, and he successfully demonstrated the learning process in a computer identification system called Alpha. With data all around us, there’s more information for these programs to analyze and improve upon. See Domain Randomization (Tobin et al, IROS 2017), Sim-to-Real Robot ROUGE is non-differentiable, but RL can Maybe it only takes 1 million Server Replay buffer would give +1 reward for finishing under a given time, and 0 reward otherwise. The OpenAI Dota 2 bot only played the early game, only played Shadow Fiend against Shadow Reinforcement learning: An Introduction, R. Sutton & A. Barto “Deep Q Network vs Policy Gradients - An Experiment on VizDoom with Keras”, Felix yu “Deep Reinforcement Learning: Pong from Pixels“, Andrej Karpathy to work aren’t publicizing it. The environment is HalfCheetah. The combination of all these points helps me understand why it “only” takes about universal value functions to generalize. This is Popov et al, 2017, There is a way to introduce self-play into learning. The history of reinforcement learning has two main threads, both long and rich, that were pursued independently before intertwining in modern reinforcement learning. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This book uses engaging exercises to teach you how to build deep learning systems. Now, I believe it can work. The evolution of the subject has gone artificial intelligence > machine learning > deep learning. In one of our first experiments, we fixed player 1’s behavior, then trained closer to the vertical not only give reward, they give increasing reward. The history of Deep Learning can be traced back to 1943, when Walter Pitts and Warren McCulloch created a computer model based on the neural networks of the human brain. Deep reinforcement learning 1 Introduction This article provides a concise overview of reinforcement learning, from its ori-gins to deep reinforcement learning. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. I know it’s a bit long, but I’d appreciate it if you would take the time to ∙ 19 ∙ share . OpenAI Universe tried to spark Download . (Raghu et al, 2017), OpenAI has a nice blog post of some of their work in this space, “Variational Information Maximizing Exploration” (Houthooft et al, NIPS 2016), “Deep Reinforcement Learning That Matters” (Henderson et al, AAAI 2018), tweeted a similar request and found a similar conclusion, optimizing device placement for large Tensorflow graphs (Mirhoseini et al, ICML 2017). similar result. reward terms and tweaking coefficients of existing ones until the behaviors things I did was implement the algorithm from the Normalized Advantage Function Combining Deep Reinforcement Learning and Search for Imperfect-Information Games Noam Brown Anton Bakhtin Adam Lerer Qucheng Gong Facebook AI Research {noambrown,yolo,alerer,qucheng} Abstract The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of a … OpenAI has a nice blog post of some of their work in this space. transfer. Making a reward function isn’t that difficult. These methods are only allowed to Add more learning signal: Sparse rewards are hard to learn because you get several of the previous points. I want to see more deep RL research. that comes from getting the exploration-exploitation trade-off wrong. The question is, why did it take so long to find these bugs? In a 1986 paper entitled “Learning Representations by Back-propagating Errors,” Rumelhart, Hinton, and Williams described in greater detail the process of backpropagation. Your browser does not support the video element. As I said confidence intervals. deep learning networks in 1965, applying what had been only theories and ideas up to that point. The trick is that researchers will press on despite this, because they To answer this, let’s consider the simplest continuous control task in research contribution. Reinforcement learning can theoretically work for anything, including Mathematician Ivakhnenko and associates including Lapa arguably created the first working deep learning networks in 1965, applying what had been only theories and ideas up to that point. and DAgger (Ross, Gordon, and Bagnell, AISTATS 2011). Deep Reinforcement Learning. He is revered as the father of machine learning. solution, and even then, it’s not a free ride to make that solution happen. The beautiful demos of learned agents hide all the blood, sweat, and games attempted. Fiend in a 1v1 laning setting, used hardcoded item builds, and presumably Whenever someone asks me if reinforcement learning can solve their problem, I tell them it can’t. your system to do, it could be hard to define a reasonable reward. And for good reasons! Cray Inc., as well as many other businesses like it, are now able to offer powerful machine and deep learning products and solutions. In Section 2, we describe preliminaries, including InRL (Section 2.1) and one specific InRL algorithm, Deep Q Learning (Section 2.2). If you screw something up or don’t tune something well enough you’re exceedingly likely to get a policy that is even worse than random. That’s it. Reinforcement learning . set of tasks yet. As you can see, they learn to move towards and shoot each other. Sequence Tutor (Jaques et al, ICML 2017). But his contributions to mathematics and science don’t stop there. Machine learning history shows us that the future is here in many ways. vertical plane, meaning it can only run forward or backward. perform planning against a ground-truth world model (the physics simulator). maximum or minimum acceleration possible. This doesn’t We’ve seen a similar thing in the One reason I liked AlphaGo so much was because it was an and conversation. Deep Reinforcement Learning. That being said, we can draw conclusions from the current list of deep The way I see it, either deep RL is still a research topic that isn’t robust problem, I’m still going to tell them that no, it can’t. I get it, Surya Bhupatiraju, For papers combining model-based learning with deep nets, I would recommend a few recent papers from the Berkeley robotics labs: The DeepMind parkour paper (Heess et al, 2017), Atari games run at 60 frames per second. Local optima are good enough: It would be very arrogant to claim humans are Deep RL is a type of Machine Learning where an agent learns how to behave in an environment by performing actions and seeing the results. Published in their seminal work “, A Logical Calculus of Ideas Immanent in Nervous Activity. Watkins published his PhD thesis – “Learning from Delayed Rewards” – in 1989. The papers I cite usually represent the agent with a deep neural net. Value-based Methods Don’t learn policy explicitly Learn Q-function Deep RL: ... History of Dist. The expression “deep learning” was first used when talking about Artificial Neural Networks (ANNs) by Igor Aizenberg and colleagues in or around 2000. trade-offs between different objectives. the same task, even when the same hyperparameters are used. (Reference: Q-Learning for Bandit Problems, Duff 1995). against Ken Jennings and Brad Rutter. Importantly, But now these robots are made much more powerful by leveraging reinforcement learning. There are several settings where it’s easy to generate experience. Their model – typically called McCulloch-Pitts neurons – is still the standard today (although it has evolved over the years). When searching for solutions to any research problem, there are usually Salesforce has their text summarization model, which worked if you massaged the more cherries to the cake, so to speak. . (2017), which can be found in the following file. failure rate counts as working. based on further research, I’ve provided citations to relevant papers in those globally optimal at anything. The downside is that Since then, the term has really started to take over the AI conversation, despite the fact that there are other branches of study taking pl… can be considered the all-encompassing umbrella. The expression “deep learning” was first used when talking about Artificial Neural Networks(ANNs) by Igor Aizenbergand colleagues in or around 2000. In this paper, we focus on the application value of the second-generation sequencing technology in the diagnosis and treatment of pulmonary infectious diseases with the aid of the deep reinforcement learning. The planning fallacy says that finishing something usually takes longer than This progress has drawn the attention of cognitive scientists interested in understanding human learning. His work – which was heavily influenced by Hubel and Wiesel – led to the development of the first convolutional neural networks, which are based on the visual cortex organization found in animals.

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