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Q-Learning algorithm is off-policy algorithm because
Policy being trained is exactly the one being executed
Policy being trained is not necessarily the one being executed
There is nothing called Q-Learning algorithm but Q-Value Iteration
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1 Reinforcement Learning - Introduction to Reinforcement Learning
2 MCQ - In Reinforcement Learning, a software agent makes observations, takes actions within an environment, and receives rewards in return
3 MCQ - A policy is the algorithm used by software agent to determine its actions.
4 MCQs- Reinforcement learning is -
5 MCQs - Which of the following is true about reinforcement learning?
6 Reinforcement Learning - Introduction to OpenAI Gym
7 MCQ - OpenAI gym is a toolkit that provides wide variety of simulations to train agents in Reinforcement Learning
8 Reinforcement Learning - Credit Assignment Problem (Part I)
9 Reinforcement Learning - Credit Assignment Problem (Part II)
10 MCQ - An action advantage is the estimate of how much better or worse an action is compared to other possible actions, on average
11 MCQ -REINFORCE algorithms were introduced by Ronald Williams in
12 Reinforcement Learning - Markov Decision Processes
13 MCQ - Markov Chains are stochastic processes with no memory
14 MCQs - Hidden Markov Model is used in Reinforcement learning.
15 Reinforcement Learning - Value Iteration Algorithm, Q-Value
16 Reinforcement Learning - Temporal Difference Learning
17 Reinforcement Learning - Q-Learning
18 MCQ - Q-Learning algorithm is off-policy algorithm because
19 MCQ - DNN that estimates Q-Values is called Deep Q-Network (DQN)
20 MCQ - The Dueling DQN algorithm was introduced in yet another 2015 paper by
21 Reinforcement Learning - TF-Agents Library
22 MCQ - TF-Agents library is a Reinforcement Learning library based on TensorFlow
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