{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-07-01T19:57:42.558827Z","iopub.execute_input":"2023-07-01T19:57:42.559240Z","iopub.status.idle":"2023-07-01T19:57:42.568690Z","shell.execute_reply.started":"2023-07-01T19:57:42.559188Z","shell.execute_reply":"2023-07-01T19:57:42.567408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Introduction","metadata":{}},{"cell_type":"markdown","source":"The last two years can safely be called the entry into the golden era of artificial intelligence. Smart dialog systems such as chatGPT communicate as well, and sometimes even better than a live interlocutor; generative systems like Midjorney or Stable Diffusion create very beautiful, aesthetic images and gradually learn to create videos; unmanned drones learn to navigate independently in the surrounding space. Humanoid robots such as Optimus or CyberOne are becoming more and more numerous, they are gaining new capabilities and are gradually getting closer to becoming a full-fledged human assistant. Entire business sectors and industries can change dramatically due to the rapid progress in the field of artificial intelligence. And, most likely, it will. \n\nIn my work, I want to talk about one of the most unusual and promising machine learning algorithms - reinforcement learning.\n","metadata":{}},{"cell_type":"markdown","source":"# What is reinforcement learning (RL)?","metadata":{}},{"cell_type":"markdown","source":"Reinforcement learning is an approach to learning a machine learning system where there is no marked-up data and the system learns in the process of interacting with a pre-created environment. Interestingly, the system learns from its own mistakes and is similar in this respect to how we humans learn new skills. And the whole world is our learning environment.\n\nThere are several areas where the use of reinforcement learning is very appropriate:\n\n● Robotics. Here, m is used to teach the robot how to walk, overcome obstacles, and interact with surrounding objects.\n● Autopilot in the car. Thanks to autopilot, the car can behave correctly on the road without human intervention. Of course, autopilot also requires computer vision, in addition to reinforcement learning.\n● Games. We are talking specifically about competing with a person in games such as Go or chess. It is thanks to reinforcement learning that machines have the opportunity to beat humans in such a complex game as Go.\n● Trading. In trading, reinforcement learning can help you develop a strategy of behavior that will lead to maximizing profits. \n\nOf course, these are not all possible areas of application of reinforcement learning. For example, reinforcement learning is also used for ChatGPT training.\n\n\n\n\n","metadata":{}},{"cell_type":"markdown","source":"# Where it is used RL?","metadata":{}},{"cell_type":"markdown","source":"Let's take a look at how well-known technology companies use reinforcement learning. And the first company will be Elon Musk's Tesla.\n\nThe simplest case of applying reinforcement learning in Tesla is to identify obstacles so that the autopilot can react in time and correctly and protect passengers. A more unobvious case of applying reinforcement learning in Tesla is intelligent energy consumption planning in order to use the car's charge as efficiently as possible. \nIn this case, reinforcement learning provides Tesla vehicles with the opportunity to learn from their own experience, adapting to various features of the surrounding behavior and driving style of a person. Thus, Tesla demonstrates the huge potential of reinforcement learning not only in the field of self-driving electric vehicles, but also in terms of application in other areas.\n\nThe next company in line is OpenAI and its main product, which made a lot of noise - ChatGPT. ChatGPT is an intelligent dialog system trained on very large amounts of data. Since GPT version 3.5,ChatGPT has become very \" human” in communication, and some researchers have even suggested the presence of intelligence in this system. And scientists from Microsoft Research conducted a large study on the search for intelligence in ChatGPT and combined the findings in the monograph \"Sparks of Artificial General Intelligence: Early experiments with GPT-4\". So what made ChatGPT so human-like in communication?\nIt's all about the Reinforcement learning from Human Feedback algorithm. According to the data on the site https://openai.com/blog/chatgpt the ChatGPT training process consisted of 3 steps:\n1.Supervised fine-tuning - SFT. Retrain the previous language model based on the first type of marked - up data-with ready-made responses.\n2.Reward model. Training the reward model based on the second type of data - people ranking different bot responses.\n3.Reinforcement learning. Using the reward model to retrain the language model with reinforcement learning tools .\nAnd so on for several iterations.\n\nWhat does Human Feedback have to do with it? It was people's feedback that helped to evaluate ChatGPT responses for compliance with the level of toxicity and correctness. Also, professional programmers evaluated the program code generated by the system. Later, with the help of Reinforcement learning, ChatGPT adapted to the required response parameters in order to receive a reward - one of the main components of training in Reinforcement learning. \n\nThe SFT from step is just supervised learning. But at the Reward model stage, Human Feedback is activated. And schematically, the stage looks like 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a result, thanks to the collected feedback, the developers received a model that, for any generated text, is able to predict how much the text meets the expectations of a person.\n\nAnd at the final stage, reinforcement learning from human feedback comes into play.r Unlike using the reward function in classical reinforcement learning, reinforcement learning from human feedback uses the Reward Model learned from people's ratings, which was obtained in the previous step.\n\nOpenAI has shown what reinforcement learning is capable of in relation to dialog systems, and as a result, we have the most advanced dialog system at the moment, which has forced the IT community and journalists to discuss that a common or strong artificial intelligence is very close.\n\nFamous for its highly realistic humanoid robots, Boston Dynamics also uses reinforcement learning to train its robots to walk upright. And this is still the most popular case of applying reinforcement learning in robotics. \nOf course, these are not all companies that are engaged in research in the field of reinforcement learning and the implementation of these algorithms in their products, but these are the most striking examples.\n","metadata":{}},{"cell_type":"markdown","source":"# Let 's analyze","metadata":{}},{"cell_type":"markdown","source":"Now let's turn to the electronic archive of scientific articles arXiv and analyze the topics of the most recent articles about reinforcement learning. \n\nFirst, you can pay attention to the variety of business and technology areas in which the research was conducted. These include NFT (\"Learning Profitable NFT Image Diffusions via Multiple Visual-Policy Guided Reinforcement Learning\"), marketing, navigation, walking training, inventory management, Autonomous Driving with Deep Reinforcement Learning in CARLA Simulation\"), robotics (\"HomeRobot: Open-Vocabulary Mobile Manipulation”) and even quantum technologies (\"Enhancing variational quantum state diagonalization using reinforcement learning techniques\").\n\nThere is also a large amount of research devoted to improving existing algorithms. For example, these articles include \"Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory Weighing\", \" Can Different Decision Trees Learn Interpretable Reward Functions?”, “Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Grouping”, “Decentralized Multi-Agent Reinforcement Learning with Global State Prediction”, “Learning from Visual Observation via Offline Pretrained State-to-Go Transformer”, “A Finite Expression Method for Solving High-Dimensional Committor Problems” и многие другие.\n\nLet's look at the total number of articles about reinforcement learning for different periods.\n\n![graph.JPG](attachment:a783998c-9f4d-4ae7-9e49-e03220ae064c.JPG)\n\nOn the graph, we see a steady increase in the number of articles in each month. Only in April there was a decrease, however, in May there is an increase in the number of studies again.\n\nNext, we will analyze the number of articles about reinforcement learning over the past 5 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mt9T1CK2ma5Zgjg5xgc8CtvXvH/h86Lcw2d0t9PNG0aQxoTuJGOeKAOkstYt9T0NdTsT5kTxl1B4OR1B98jFUfCHiGTxNof2+a3W3bzWTYjFhx3zWd4L0u60n4fmG+QxyyLJL5bdUBHAP5Z/GoPhV/wAiZ/28yf0oA7WiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA4PT/wDktOpf9eg/ktd27rGheRgqqMlmOABXI6x4C/tTxBNq0Gs3VjLMoUiFQMAADrn2qofhn9oIXUfEWpXUPeNmxn9TQBX8NyDxB8T9S1q3+aztYvJjkHRj04/U/lVjxYv9heNtH8QJ8sMrfZbo+x6E/n+ldbpOkWWiaelnpsIihXnHUsfUnua5r4jX+nP4UuLOSaOW6kdVhhRwX8wN6e3NAEfhJDrXi/WfEMnzRo/2S1P+yvUj9PzNMtR/xeq8/wCvAf8AstdH4X0kaJ4bs7HGHRMye7nk/rUcXhuOLxhNr4uXMksAhMO0YA45z+FAGL8U/wDkTl/6+o/61L4m8SXej2ek2GlRRvfahtSN5fuJwBn9a1/E3h9PEukiwluGt1Eqyb1UE8dufrTNe8K2PiHTYLW7aRJLfHkzxnDIcY/pQBg32g+JG06e41rxZ5MKxs0kdtCEHTpuPNUvBP8AySnVP+3j/wBArWh+H6Ssq63reo6rAhysE0pCfiM81f0fwlHo/h++0mO8eSG6LkMUAMe4YwPWgCv8NxjwJY49X/8AQjWf4Q/5KD4q/wCuq/zNdN4e0VPD+iQadHM06w5w7LgnJz0qDSfDcela9qeppcvI2oMGaMqAEx6Hv1oA57xT/wAlQ8MfRv60tx/yWq0/68T/ACNb+p+Go9S8S6brDXLxvYZ2xBQQ+fU9qWTw3HJ4xi8QfaXEkcJhEO0YPB5z+NAHO+GWFt8UPEVvcnbNPteLP8S9ePwI/Kun1/xDZeHbWKe/Ej+dII0SJQWJPsSKreIPCNjr80V00k1pfQjEd1bttYfX1qnpvgSC31OPUNW1K71e4h5hN02Qh9cUAU9b0XRfE3iV47PUZ7DXLaIF2hyDjtn169jWbeXnijwNc2b6hqcerafPMItrjEg/r+prptf8G2et3yX8dxPYX8YwLm3bBI96q2HgKCLUYr3WNTvNXmgOYhdPlUPrjvQB1gOQDXBeMmFp8QfDd7dHbbBim89FbP8A9cV3tZ+taJY6/p7WepRb4ycqQcMh9QexoAuTTRwW7zTMqxopZmJ4AFcB8P1ZvC2vXKgiCeaUxe42/wD16u/8K4EiLbXfiDU57BTxatJ8uOw/yK6qHS7W10g6bZRiC3EZjVVH3QR19zzQBzfwu/5EeH/rtJ/OuwrJ8NaCnhvRU06KdrhVdm3soBOT6CtagDhdD/5K7r3/AF7p/wCy0IP+L2Sf9eA/kK0dX8EJqGvHV7HVLrTbp1AkMB+/j/8AVV5PDaL4wbX/ALS5kaDyTDtGPrmgDJ+KX/IlP/18R/zNYnj+HK+F5ZppLe2BCPPGcNGSF+YHscA/lXa+JtATxJo50+W4a3UyK+9VBPH1qe+0Sz1PRRpmoR+dAEC+hBA4IPY0Ac4PAUhQOPFmtlSMhhdcY9aueCtJ0vS7a8/sfVG1JZZQZXZlbawHqKoD4cssX2VfEmqCw6fZQ/y7fT/IrqNI0ey0PTkstOi8uJeTk5LHuSe5oAvVw3w1Hza//wBhBv613NYvh3w5H4e+3eXcvP8AbLgzncoG0ntQBgeK/wDkpPhf/eb+dN1RhF8ZdKZ+A9mVUk9T81dDqnhuPU/EOm6q1y8baeSVjCgh8+p7UzxJ4UsvEqQtPJLb3Nucw3EJwy/40AUPiXPHF4Gu1kYBpWRUH947gf5Csjxl/wAkm0/6W/8A6DV+X4cQ3lnMmqaxfX9wybIp7ht3k+4XPWtXV/CsereF7fRXuniSERgShASdgx0oAvjjw2Mf8+n/ALJXO/C3/kSl/wCviT+Yrq/so/s/7LuOPK8vdj2xms/wzoCeGtHGnxXDXCiRn3soB5+lAHNeH/8AkrniH/rkv/stO8C3EI8VeJ7csBM11vA7lQSP6/rW/YeGo7HxVf62ty7veqFMRUAJjHf8KypPh3aveXl7HqF1BeXE5mjuITsaHPVeOooAp/E91mh0ixi+a7lvFaNR1wOM/mRWZ4k0/wC0/FS2hur+509bm1VYri3fY24Ajbn3NdTovgm10vUv7Svby41S/Aws902dg9h6+9X/ABB4a0/xJaLDqCMHjOYpozh0PsaAOevPAiQ2csl94t1hbdVPmGW5+Xb3zntVXXrCy034QT2+l3hvLUFTHMSDuzIPTirv/CuvtGyLVfEGpX1ohBFu8mF46dzW7rHhy21Twy2iQt9jtyqqvlrnYFIPT8KALGgjHh3T8f8APtH/AOgitCoLG1FlYQWqsXEMaxhiMZwMZqegDgvCTra/EXxJa3LbbiaQSRhurLknj8CK0viTcwQeB7uOYjfMVSNT1LbgePwFXPEHg+x1+eO7aSazvohhLq3bDD6+tULP4f2wv47zW9SvNYkhOY1unyq/h3oAxfGEMkHwp0uKfIkUwBge3FeiW/8Ax6xf7g/lWX4n8PR+JdJFjLcNbqJFk3ooJ47c1rRp5capnO0AZoAoa/qy6HoN1qLx+Z5CZCf3jnAH5muV0238Y+I9Phv312302Gcb44re3DkKfUmu0vrKDUrGazvI/MgmUq6+orkYPh0bZTb2/iPVIrEk/wCipJgYPbP/ANagDJ8HwfZfihqcBv5NQaO3w9xIclmyufyPFaPhL/ko3if/AHx/OtXSPBFloWvjUdMmkij8jyWgI3Bv9osec5q1pXhuPS/EGpaqly8jX5BaMqAE57HvQBh/FT/kXLP/AK/Y/wCRqX4mf8iFJ/10i/nW74i0C28SaS1jds8Y3B0kTqjDvWTL4Je58Ky6LeazdXKvKsizyruZAP4Rk9KAMXxv+5svCl3N/wAe0M0ZlPYcKf6GvQnnjS3M7OoiVd5fPGMZzVO70a01DRRpd/H58HlqhzweBwR6GuY/4VuDGLVvEGptpwP/AB6eZ8uPT/IoAoeM9etfEXw5kvLGOVIReIg81QCSD1GCeKi+IkROl+G5pJJIbdXVZJYzzHlV+Ye4ANdXq/hGz1LwzHols5sraNlZTGu7GPr6+taN3o9pqGj/ANm38Yng2BDng8Dgj0NAHMp4DeSNZI/FutsjDcGF1wR60aBpvh/RNJ1iZNXbUrST/j8aRg+3qDnaM5OaaPhy0cRtYPEeqRWB4+zB+Men+RXR6X4e03SNHOmWluPszgiQPyZMjBLHvQBx1n4Ln+yJf+CfEtxbW0wLxxSZKHP+e4JrV8GeINUvtQ1HR9c8qS708gGeLo46VEPh39ld10fxBqWn2znJgikyo/UVveH/AA3Y+HLV4rEO7ytulmlbLyH3NAGq4JjYDqRxXCfC6RIrPVbGUgXcN4zSIeuDxn8wa7yuZ1nwPZ6nqR1Kyu7nS78/entWxu9yPWgDK+Kcsb6Xp1ihDXU14pjQdcDIz+opPE6lfiF4UV+SCc/WtXR/A9np2pDUr+7udUvl+5NdNnZ7getXdU8Nx6n4h03VXuXjbTySsYUEPn1PagBPGX/Imap/17tUfgb/AJEfS/8Arj/U1p6vpy6to9zYPIYluIyhdRkrmm6Lpi6NottpyStKtum0OwwW59KAJ73/AJB9x/1yb+Vcp8LP+RJj/wCu8n867F1DoyMMqwwR6iuY0HwUPD2rNcWOq3RtCWP2JvuZP9RQBm/DL/V65/1/tS+KP+Sm+GP+B1v+HPDcfh1b0R3Lz/a5zMdygbSe1GpeHI9S8R6bq7XLxvYZ2xhQQ+fU9qAOH1DTPtvxau7W41K601riFWhltpNjPwPlz6cH8q19Q8CwQWEr6p4t1ZbXGJDPcjZj3zxXQ+IvCun+JI4zd74riH/VXERw6f4isZfh0tzLH/beu6jqcEZysE0mF/maAOh8PWltYeH7O2sbk3VtHHiOYkHevXPFQ+Ktd/4R3w9PqAi851IVEPQsTgZ9q1YokghSKFAkaKFVVGAAO1VtV0u11nTZbG/TfDKMEA4I9CD60AcpaaZ4x1iyhu7vxFDYJMgcRW1uGwpGep74rO+HcaxeMtfjW7e8CYX7Q5yZMN1JrTg+HTRRi2bxHqjWA4+yrJtXHp/kVp6H4OtPD2tXN7p0rJDcRqn2YjIXGOd3X/8AXQBkfDz/AJDHiY/9P39WrpvEOu23h3Rpb66IO0YjjzzI3YCoNA8Nx6DdajPHcvMb+bzmDKBs68D161X1jwjHruu2t9qN5JJbWpylnsGwn1J70Acja+CL3xLod5rWqSMmr3hE1sCcCNR0Htn9OK6XwL4mfWdPex1HKanY/JMrcFgON39D/wDXrqwMDA4Fc5eeD4pfFcWvWN5JZXK8SqiArN65+o4oAydH/wCSwa1/16p/7LRruPD3xF03WM7LbUENrcHsG7E/p+Vb9p4bjtPFl5rouXZ7qIRmEqMLjHOfwp/iXw/B4l0drC4kaH5w6SKMlSKAOf8AAyHVtY1jxLMOLmYwW+e0a/5H5V29UdF0qHRNHt9PtyWSBdu4jBY9yavUAcZrfiDWLvxcPDvh029vKkXmTXNwM4GM4A+hFYfjTRdXsvDE9xrfieS7G5RHbJEI0dieh9fWur8QeC7XW9Qj1GG7uNPv4wFFxbnBI9/8aoyfDm1u7WUatql9qFyyFI57iTd5XuF9aAM3xRx8HbHPTyrf+ldzpU8Nxo1pNAytE0KlSDxjFZN74TS/8HwaDNevthCAThBkhTxxWbN8O4/mhsNa1CyspP8AWWkUnyN6454zQBi6TG+pal42udPy0MqNHGy9GbB6f5710Pw0uIZvA9rHEw3ws6Sr3VtxPP4Gt7R9GstC01LLTovLiXk5OSx7knuawL34f2zahJe6LqV5o8sxzKLV8K34dqAM/wAYSLdfEHw1aWxDXEUpkkC9VXI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How far to AGI?","metadata":{}},{"cell_type":"markdown","source":"Over the past few years, the topic of strong or general artificial intelligence (AGI) has become popular. It is an artificial intelligence capable of performing many different tasks, capable of thinking and making decisions independently. That is, in terms of development, it should be approximately at the level of a person or even higher. An example of such an AI is Ultron from the movie “Avengers: Age of Ultron\". And it would be logical to assume that reinforcement learning will be the key to creating AGI. But is it true?\n\nIn fact, this may well be the case. Reward is the foundation of reinforcement learning. If the algorithm did everything correctly, it gets a reward, if not, it doesn't get a reward. And after all, our whole life is based on such rewards or incentives. In fact, all human actions are aimed at receiving some kind of reward-money, pleasure, pleasant emotions, praise, and many others. This theory is described in more detail in the study \"Reward is enough“, conducted by scientists from the British laboratory”DeepMind\".\n\nOf course, reinforcement learning requires significant development, compared to the current level, to become the basis of the very AGI that science fiction writers, philosophers and visionaries talk about. \n\n**But these are plans for tomorrow. And today we have reinforcement learning-these are machine learning algorithms that are already changing entire areas of business, thanks to products created using them. And this is just the beginning**\n","metadata":{}},{"cell_type":"markdown","source":"# The final step to success","metadata":{}},{"cell_type":"code","source":"submission = pd.read_csv(\"/kaggle/input/2023-kaggle-ai-report/sample_submission.csv\")\nsubmission.loc[0]['value'] = 'Other'\nsubmission.loc[1]['value'] = 'https://www.kaggle.com/code/evgenykuznetsov/reinforcement-learning-is-agi-already-close'","metadata":{"execution":{"iopub.status.busy":"2023-07-01T19:57:47.423065Z","iopub.execute_input":"2023-07-01T19:57:47.424232Z","iopub.status.idle":"2023-07-01T19:57:47.435093Z","shell.execute_reply.started":"2023-07-01T19:57:47.424162Z","shell.execute_reply":"2023-07-01T19:57:47.434200Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-07-01T19:57:48.897643Z","iopub.execute_input":"2023-07-01T19:57:48.898101Z","iopub.status.idle":"2023-07-01T19:57:48.905388Z","shell.execute_reply.started":"2023-07-01T19:57:48.898065Z","shell.execute_reply":"2023-07-01T19:57:48.904145Z"},"trusted":true},"execution_count":null,"outputs":[]}]}