Model types, Algorithms and approaches, Function approximation, Deep reinforcement-learning, Deep Multi-agent Reinforcem

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 following

1. Get state-of-the-art knowledge regarding

   1. Model types

   2. Algorithms and approaches

   3. Function approximation

   4. Deep reinforcement-learning

   5. Deep Multi-agent Reinforcement-learning

2. Validate your knowledge by answering short and very short quizzes of each lecture.

3. Be able to complete the course by ~2 hours.

Syllabus

1. Model types

   1. Markov decision process (MDP)

      A discrete-time stochastic control process.

   2. Partially observable Markov decision process (POMDP)

      A generalization of MDP in which an agent cannot observe the state.

   3. Decentralized Partially observable Markov decision process (Dec-POMDP)

      A generalization of POMDP to consider multiple decentralized agents.

2. Algorithms and approaches

   1. Bellman equations

      A condition for optimality of optimization of dynamic programming.

   2. Model-free

      A model-free algorithm is an algorithm which does not use the policy of the MDP.

   3. Off-policy

      An off-policy algorithm is an algorithm that use policy 1 for learning and policy 2 for acting in the environment.

   4. Exploration-exploitation

      A trade-off in Reinforcement-learning between exploring new policies to use existing policies.

   5. Value-iteration

      An iterative algorithm applying bellman optimality backup.

   6. SARSA

      An algorithm for learning a Markov decision process policy

   7. Q-learning

      A model-free reinforcement learning algorithm to learn the value of an action in a particular state.

3. Function approximation

   1. Function approximators

      The problem asks us to select a function among a well-defined class that closely matches (“approximates”) a target function in a task-specific way.

   2. Policy-gradient

      Value-based, Policy-based, Actor-critic, policy-gradient, and softmax policy

   3. REINFORCE

      A policy-gradient algorithm.

4. Deep reinforcement-learning

   1. Deep Q-Network (DQN)

      A deep reinforcement-learning algorithm using experience reply and fixed Q-targets.

   2. Deep Recurrent Q-Learning (DRQN)

      Deep reinforcement-learning algorithm for POMDP extends DQN and uses LSTM.

   3. Optimistic Exploration with Pessimistic Initialization (OPIQ)

      A deep reinforcement-learning for MDP based on DQN.

   4. Value Decomposition Networks (VDN)

      A multi-agent deep reinforcement-learning algorithm for Dec-POMDP.

   5. QMIX

      A multi-agent deep reinforcement-learning algorithm for Dec-POMDP.

   6. QTRAN

      A multi-agent deep reinforcement-learning algorithm for Dec-POMDP.

   7. Weighted QMIX

      A deep multi-agent reinforcement-learning for Dec-POMDP.

Resources

• Wikipedia

• David Silver’s Reinforcement-learning course

Aprendizaje por refuerzo moderno con aprendizaje profundo

Model types, Algorithms and approaches, Function approximation, Deep reinforcement-learning, Deep Multi-agent Reinforcem”

Este curso es GRATIS

Curso gratis en Udemy

Con los cursos gratis de Udemy puedes aprender muchas cosas sin tener que gastar en ello. Pero primero debes tener en cuenta varias cosas:

Contenido Gratuito

Los cursos gratis de Udemy te permiten aprender nuevas cosas sin tener que pagar. Aprovecha la oportunidad.

Anuncios

Aprendizaje

Pon en práctica todos tus conocimientos aprendidos. Realiza increíbles proyectos basados en el mundo real.

Limites

Debes tener en cuenta que todos los cursos gratuitos de Udemy son de máximo 2 horas y no incluyen un certificado.

Este curso se encuentra disponible de manera gratuita sin necesidad de ningún cupón, a través de la opción “GRATIS”.

Te recomendamos primero leer las diferencias entre un curso gratis y uno de pago para evitar malentendidos:

Deja tus comentarios y sugerencias

Sobre Facialix

Facialix es un sitio web que tiene como objetivo apoyar en el aprendizaje y educación de jóvenes y grandes. Buscando y categorizando recursos educativos gratuitos de internet, de esta manera Facialix ayuda en el constante aprendizaje de todos.