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A machine learning technique influenced by behavioral psychology which can be described as "learning by trial and error."

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1answer
12 views

Difference between state, model, and environment in reinforcement learning

In particular I would like to have a simple definition of environment and state.what are the differences between those two concepts? Also I would like to know how the concept of model relates to the ...
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2answers
28 views

Does Monte Carlo Search (specifically used by AlphaZero) Qualify as Machine Learning?

To the best of my understanding, Monte Carlo Search is an alternative method to Minimax for searching a tree of nodes. It works by choosing a move (generally the one with the highest chance of being ...
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1answer
92 views

Is the discount not needed in a deterministic environment for Reinforcement Learning?

I'm now reading a book titled as "Deep Reinforcement Learning Hands-On" and the author said the following on the chapter about AlphaGo Zero: Self-play In AlphaGo Zero, the NN is used to ...
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1answer
57 views

How to generalise over multiple simultaneous dependent actions in Reinforcement Learning

I am trying to build an RL agent to price paid-for-seating on commercial flights. I should reiterate here - I am not talking about the price of the ticket - rather, I am talking about the pricing you ...
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0answers
24 views

Optimization step in Apprenticeship Learning via Inverse Reinforcement Learning

Why the optimization step of the algorithm a quadratic program? [See: Apprenticeship Learning via Inverse Reinforcement Learning; page 3] Isn't the objective function linear? Why don't we treat ...
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2answers
39 views

Reinforcement Learning Batch Size

I am using a neural network as my function approximator for reinforcement learning. In order to get it to train well I need to choose a good learning rate. Hand picking one is difficult, so I read up ...
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1answer
73 views

Reinforcement Learning (Fitted Q): Qn on Concept & Implementation

I hope to get some clarifications on Fitted Q-Learning ('FQL'). My Research So Far I've read Sutton's book (specifically, chp 6 to 10), Ernst et al and this paper. I know that ...
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1answer
25 views

Which is more important, doubt or reinforcement?

Reinforcement? We hear much about reinforcement, which is, in my opinion a poor choice of a term to describe a type of artificial network that continues to acquire or improve its behavioral ...
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1answer
22 views

RL: how can the cart-pole problem be a continuing task?

In Introduction to Reinforcement Learning (2ed), Sutton and Barto, there is an example of Pole-Balancing problem (Example 3.4). In this example, it said, this problem can be treated with 'episodic ...
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1answer
26 views

Exploration Strategies for Reinforcement Learning w/ Continuous Action Space

I'm building a deep neural network to serve as the policy estimator in an Actor-Critic reinforcement learning algorithm for a continuing (not episodic) case. I'm trying to determine how to explore ...
2
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1answer
34 views

Does epsilon-greedy approach always choose the “best action” (100% of the time) when it does not take the random path?

I'm now reading the following blog post but on the epsilon-greedy approach, the author implied that the epsilon-greedy approach takes the action randomly with the probability epsilon, and take the ...
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1answer
44 views

What is the difference between Actor-Critic and Advantage Actor-Critic?

I'm struggling to understand the difference between Actor-Critic and Advantage Actor-Critic. At least I know they are different from Asynchronous Advantage Actor-Critic (A3C), as A3C adds ...
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1answer
33 views

What is the difference between policy and action in Reinforcement Learning?

I'm confused with the two terminology - action and policy - in Reinforcement Learning. As far as I know, the action is: It is what the agent makes in a given state. However, the book I'm reading ...
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1answer
24 views

What is the “trajectory” in the Reinforcement Learning?

I'm now learning about the Reinforcement Learning but just found the word "trajectory" in this answer. However, I'm not sure what it means. I read a few books on the Reinforcement Learning but none ...
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0answers
15 views

What is a copy of network to create a “target network” for in DQN? [duplicate]

I'm now reading a book titled "Hands-On Reinforcement Learning with Python" by O'Reilly, and the author said the following to implement the DQN algorithm. To make training more stable, there is a ...
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2answers
50 views

Reinforcement Learning with asynchronous feedback

I want suggestions on literature on Reinforcement Learning algorithms that perform well with asynchronous feedback from the environment. What I mean by asynchronous ...
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1answer
21 views

A3C & TWEAN (NEAT)

As a amateur researcher and tinkerer, I've been reading up on neuro-evolution networks (e.g. NEAT) as well as the A3C RL approach presented by Mnih et al and got to wondering if anyone has ...
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0answers
19 views

Reinforcement Learning in Game Javascript

I really want to understand how reinforcement learning works. I built a simple game to test this. There are squares falling from the sky and you have the arrow keys to escape. How could I code the ...
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1answer
158 views

Learning algorithms of Neural Networks

Could you please let me know which of the following classification of Neural Network's learning algorithm is correct? The first one classifies it into: supervised, unsupervised and reinforcement ...
3
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1answer
42 views

Can the optimal value of discount factor in Deep Reinforcement Learning be between 0.2 to 0.8?

I'm now reading a book titled as Hands-On Reinforcement Learning with Python, and the author explains the discount factor that is used in Reinforcement Learing to discount the future reward, with the ...
4
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1answer
56 views

What would be the best approach to teach an AI to learn how to “sing” along a beat?

I have heard and read about HyperGAN, LSTM and a few other techniques, but I have a hard time piecing the overall concept together. End Goal Being able to input an instrumental and get an output of ...
2
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1answer
32 views

Can the opponent's turn affect the reward for a DQN agent action?

I made an engine for a 2 players card game and now I am trying to make an environment similar to OpenAI Gym envs, to ease out the training. I fail to understand this thing however: If I use ...
3
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1answer
28 views

Maximizing or Minimizing in Trust Region Policy Optimization?

I happened to discover that the v1(19 Feb 2015) and the v5(20 Apr 2017) versions of TRPO papers have two different conclusions. The Equation (15) in v1 is minimize_θ...
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0answers
21 views

Gradient of boltzmann policy over reward function

I'm struggling with an inverse reinforcement learning problem which seems to appear quite often around the literature, yet I can't find any resources explaining it. The problem is that of calculating ...
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0answers
54 views

Comprehensive list of MOOCs and books on Reinforcement Learning

I'm actually trying to learn more about reinforcement learning but I've some trouble to find good resources. Right now I'm in the condition where I'm not so good on the topic to fully understand the ...
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1answer
39 views

Can DQN announce it has things in its hand in a card game?

More informations on the card game I'm talking about are in my last question here: DQN input representation for a card game So I was thinking about the output of the q neural network and, aside from ...
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1answer
29 views

Taking into consideration “Number of Steps” in RL

I am currently implementing this paper in Python. While reading about the reward scheme I came across the following: Finally, the proposed reward scheme implicitly considers the number of steps ...
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1answer
28 views

Gradient descent update

In the following, I put the link for the general algorithm of maximum entropy inverse reinforcement learning. http://178.79.149.207/assets/maxent/maxent_slide.jpg This uses a gradient descent ...
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1answer
63 views

Representing inputs and outputs for a card game neural network

I'm attempting to create an AI for a card game using reinforcement learning. The basics of the game are that you can have (theoretically) up to 35 cards in your hand, you can also have to up to 35 ...
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1answer
44 views

DQN input representation for a card game

In order to learn about DP and RL, I chose to start a side project where I would train an AI to play a "simple" card game. I will be doing this using the DQN with replay memory. The problem is, I can'...
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0answers
27 views

Why do we have to solve MDP in each iteration of Maximum Entropy Inverse Reinforcement Learning?

Gradient in Maximum Entropy IRL requires to find the probability of expert trajectories given the reward function weights. This is done in the paper by calculating state visitation probabilities but I ...
5
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4answers
112 views

What's a good resource for getting familiar with reinforcement learning?

I am familiar with supervised and unsupervised learning. I did the SaaS course done by Andrew Ng on Coursera.org. I am looking for something similar for reinforcement learning. Can you recommend ...
5
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1answer
210 views

Why does DQN require two different networks?

I was going through this implementation of DQN and I see that on line 124 and 125 two different Q networks have been initialized. From my understanding, I think one network predicts the appropriate ...
2
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1answer
41 views

Number of Neuron in Q-Learning of Chess

So I just read about deep Q-Learning which is using a neural network for optimization instead of Q-table. I saw the example here: https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html and he ...
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1answer
28 views

analogy between reinforcement learning and webservers?

I've recently come across the client-server model , from my understanding , the client requests the server for which the server responds with a response in this case both the request and responses are ...
3
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1answer
90 views

Finding goals in Hierarchical Reinforcement Learning

In a recent paper Data-Efficient Hierarchical Reinforcement Learning, O Nachum, S Gu, H Lee, S Levine, 2018, a promising agent controlling technique called Hierarchical Reinforcement Learning was ...
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0answers
31 views

Q Learning with Multiple Agents Design

Can anyone recommend a reinforcement learning algorithm for a multi agent environment. In my simplified example, I'm implementing a Q-Learning system with different 10 agents. The agents compete for ...
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1answer
28 views

Can Q-learning working in a multi agent environment where every agent learns a behaviour independently?

I am currently exploring multi-agent reinforcement learning. I have multiple agents that communicate with each other and a central service that maintains the environment state. The central service ...
2
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1answer
68 views

Reinforcement Learning over an MDP that is actually a POMDP

Look at Breakout: We know that the underlying world behaves like an MDP, because for the evolution of the system it just need to know which is the current state, i.e. position, speed and speed ...
2
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1answer
38 views

Action Probability with Thompson Sampling in Deep Reinforcement Learning

In some implementations of off-policy Q learning we need to know the action probabilities given by the behavior policy mu(a) (e.g., if we want to use importance ...
2
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0answers
80 views

Reinforcement Learning in Commercial Strategy Games

I'm a professional game developer investigating the potential for using reinforcement learning to build strategy game AI opponents that have more creative behavior compared to traditional techniques ...
2
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1answer
36 views

How to implement a contextual reinforcement learning model?

In a reinforcement learning model, states depend on the previous actions chosen. In the case in which some of the states -but not all- are fully independent of the actions -but still obviously ...
3
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1answer
84 views

Issue with simple game AI

A few months ago I made a simple game that is similar to the dinosaur game in Google Chrome - you jump over obstacles, or don't jump over levitating obstacles, and jump to collect bitcoins, which can ...
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0answers
27 views

Implementing AI/ML in customer service

(I originally posted the question on stack overflow but someone directed me here. So this is my first post here) I am working on a task where I am required to automate the customer service request ...
2
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0answers
55 views

When can we say an RL algorithm learns an Attari game?

If a Attari game's rewards can be between -100 and 100, when can we say an agent learned to play this game? Should it get the reward very close to 100 for each instance of the game? or it is fine if ...
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1answer
88 views

Why does self-playing TicTacToe not become perfect?

I trained a DQN that learns TicTacToe by playing against itself with a reward of -1/0/+1 for a loss/draw/win. Every 500 episodes I test the progress by letting it play some episodes (also 500) against ...
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0answers
40 views

reinforcement learning rmsprop does not improve, average reward through time oscillates

I have a reinforcement learning project using policy gradient method with rmsprop optimization. (used vanilla REINFORCE algorithm)(the game is a simple pong game in open-ai gym atari environment) the ...
2
votes
1answer
40 views

Implementing experience replay in reinforcement learning

I've been reading Google's DeepMind Atari paper and I'm trying to understand how to implement experience replay. My question is that whether we update the ...
2
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1answer
54 views

Understanding experience replay in reinforcement learning

I've been reading Google's DeepMind Atari paper and I'm trying to understand the concept of experience replay. Experience replay comes up in a lot of other ...
2
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2answers
40 views

How to see terminal reward in self-play reinforcement learning?

there seems to be a major difference how the terminal reward is received/handled in self-play RL vs "normal" RL which confuses me. I implemented TicTacToe the normal way, where a single agent plays ...