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Modern Reinforcement-learning using Deep Learning | Free Udemy Course
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Modern Reinforcement-learning using Deep Learning | Free Udemy Course

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Model types, Algorithms and approaches, Function approximation, Deep reinforcement-learning, Deep Multi-agent Reinforcem - Free Course | Free Udemy Course

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Course Language EnglishCourse Caption English [Auto]Course Length 41:32 to be exact 2492 seconds!Number of Lectures 23
This course includes:
  • 42 mins hours of on-demand video
  • 19 additional resources

Hello I am Nitsan Soffair, A Deep RL researcher at BGU.In my Deep reinforcement-learning course you will learn the newest state-of-the-art Deep reinforcement-learning knowledge.You will do the followingGet state-of-the-art knowledge regardingModel typesAlgorithms and approachesFunction approximationDeep reinforcement-learningDeep Multi-agent Reinforcement-learningValidate your knowledge by answering short and very short quizzes of each lecture.Be able to complete the course by ~2 hours.SyllabusModel typesMarkov decision process (MDP)A discrete-time stochastic control process.Partially observable Markov decision process (POMDP)A generalization of MDP in which an agent cannot observe the state.Decentralized Partially observable Markov decision process (Dec-POMDP)A generalization of POMDP to consider multiple decentralized agents.Algorithms and approachesBellman equationsA condition for optimality of optimization of dynamic programming.Model-freeA model-free algorithm is an algorithm which does not use the policy of the MDP.Off-policyAn off-policy algorithm is an algorithm that use policy 1 for learning and policy 2 for acting in the environment.Exploration-exploitationA trade-off in Reinforcement-learning between exploring new policies to use existing policies.Value-iterationAn iterative algorithm applying bellman optimality backup.SARSAAn algorithm for learning a Markov decision process policyQ-learningA model-free reinforcement learning algorithm to learn the value of an action in a particular state.Function approximationFunction approximatorsThe problem asks us to select a function among a well-defined class that closely matches ("approximates") a target function in a task-specific way.Policy-gradientValue-based, Policy-based, Actor-critic, policy-gradient, and softmax policyREINFORCEA policy-gradient algorithm.Deep reinforcement-learningDeep Q-Network (DQN)A deep reinforcement-learning algorithm using experience reply and fixed Q-targets.Deep Recurrent Q-Learning (DRQN)Deep reinforcement-learning algorithm for POMDP extends DQN and uses LSTM.Optimistic Exploration with Pessimistic Initialization (OPIQ)A deep reinforcement-learning for MDP based on DQN.Value Decomposition Networks (VDN)A multi-agent deep reinforcement-learning algorithm for Dec-POMDP.QMIXA multi-agent deep reinforcement-learning algorithm for Dec-POMDP.QTRANA multi-agent deep reinforcement-learning algorithm for Dec-POMDP.Weighted QMIXA deep multi-agent reinforcement-learning for Dec-POMDP.ResourcesWikipediaDavid Silver's Reinforcement-learning courseWho this course is for:Anyone who interests in Deep reinforcement-learning

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3 Lectures | 03:08
7 Lectures | 05:13
3 Lectures | 03:08
3 Lectures | 04:50
4 Lectures | 05:24
3 Lectures | 19:49
(8 course ratings)






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