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Overview. What is Reinforcement Learning? Markov Decision Processes. Q-Learning. Policy Gradients. Reinforcement Learning.
Oct 9, 2014 · The document outlines key elements of reinforcement learning including states, actions, rewards, value functions, and explores different methods for solving reinforcement learning problems including dynamic programming, Monte Carlo methods, and temporal difference learning.
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Maxim Lapam, Deep Reinforcement Learning Hands-On: Apply Modern RL Methods, with Deep Q-networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More
Sep 12, 2018 · Dr. Subrat Panda gave an introduction to reinforcement learning. He defined reinforcement learning as dealing with agents that must sense and act upon their environment to receive delayed scalar feedback in the form of rewards.
Reinforcement Learning: Goal: Maximize 𝑖=1∞𝑅𝑒𝑤𝑎𝑟𝑑(𝑆𝑡𝑎𝑡𝑒𝑖, 𝐴𝑐𝑡𝑖𝑜𝑛𝑖) Data: . 𝑅𝑒𝑤𝑎𝑟𝑑𝑖, 𝑆𝑡𝑎𝑡𝑒𝑖+1=𝐼𝑛𝑡𝑒𝑟𝑎𝑐𝑡(𝑆𝑡𝑎𝑡𝑒𝑖,𝐴𝑐𝑡𝑖𝑜𝑛𝑖) TD-Gammon – Tesauro ~1995. P(win) Net with 80 hidden units, initialize to random weights. Select move based on network estimate & shallow search. Learn by playing against itself.
Dec 3, 2023 · The document discusses reinforcement learning, which is a machine learning technique where an agent learns from interacting with an environment. The agent performs actions and receives rewards or penalties as feedback to learn which actions yield the best outcomes.
May 24, 2019 · You can sequence through the Reinforcement learning lecture video and note segments (go to Next page). You can also (or alternatively) download the Chapter 11: Reinforcement learning notes as a PDF file.
Learn about reinforcement learning from Berkeley AI's lecture slides, covering topics such as Q-learning, exploration and policy iteration.
Q-learning. learns action-utility function (Q(s; a) function) does not need to model outcomes of actions. function provides expected utility of taken a given action at a given step. Reflex agent. learns policy that maps states to actions. passive reinforcement learning. State Map. Setup. Stochastic. Reward Function. Movement. R(s)
