Questions tagged [reinforcement-learning]

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Reinforcement learning with 0 rewards and costs

Suppose we have a hallway environment, i.e, $N$ nodes from left to right, and we can either move left or right. Moving left at the leftmost node does nothing and reaching the right most node gives you ...
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4 views

Inferring reward function and transition model from optimal policy

Consider an MDP where the transition model and the reward function are unknown. Consider an optimal policy $\pi^*$ generated from this MDP (say by some oracle who does know the transition model and ...
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1answer
19 views

Motivation for Inverse Reinforcement Learning

Learning a policy from sparse reward information (a reward function where a positive reward is only generated at the goal state) is challenging due to the resulting sparse feedback. One solution is to ...
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20 views

How to get best path from a set of path using Q-Learning?

I have 10 data sets (lat and long) of the same path. I started from point A and stopped in point B and did this 10 times for a single route to get the data. While collecting the data, there were some ...
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22 views

Discrete action space for MADDPG

I am trying to apply MADDPG, a policy gradient algorithm that uses centralized training and decentralized execution, to a project. In the work of Lowe et al., the actor returns a pmf for a discrete ...
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20 views

Resources on Dynamic Programming with Indefinite Recursion

I am trying to explain the value iteration method that is used in reinforcement learning. The method is used to estimate a solution to a recursive equation like: $ Return(state_t,action_t) = Reward(...
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1answer
81 views

Transition Function in MDP

I got a question about who and how sets the transation function values in markov decision processes? I mean when some says that an agent, in real world grid, is going to step up by %80 and left/right ...
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1answer
531 views

Machine learning approach to auction game

I am newbie with machine learning. In order to learn more I decided to try solving a specific problem/game that I have in mind. The problem is the following: I have a list of $N$ items which are ...
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1answer
276 views

reinforcement learning in gridworld with subgoals

Andrew Ng, Daishi Harada, Stuart Russell published a conference paper entitled Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. There is a specific example ...
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15 views

Where can I learn theoretical aspect of Reinforcement learning

Every RL paper has a section with some finite sample analysis/error bound/ convergence proofs. I have difficulty fully understanding such proofs and coming up with my own analysis for my personal ...
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28 views

Proving facts in inverse reinforcement learning [closed]

I was going through paper titled "Algorithms for Inverse Reinforcement Learning" by Andrew Ng and Russell. It states following basics: MDP $M$ is a tuple $(S,A,\{P_{sa}\},\gamma,R)$, where ...
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1answer
29 views

Reinforcement learining basics - is it possible to deal with environment with random events

As part of data science course I would like to solve particular problem with reinforcement learning algorithm. I believe I understand general concept, however the problems I had read about up till now ...
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1answer
29 views

Policy dependent on initial state distribution in finite horizon MDPs

Consider an MDP defined as the tuple $\langle S,A,R,P,\mu,\lambda\rangle$ where $S$ is the state space, $A$ the action space, $R:S\times A\times S\to\mathbb{R}$ the reward function, $P$ the transition ...
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37 views

Reinforcement learning and Graph Neural Networks: Issue with entropy [closed]

I am currently working on an experiment to link reinforcement learning with graph neural networks. This is my architecture: Feature Extraction with GCN: there is a fully meshed topology with ...
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2answers
4k views

Q-learning in a Dynamic environment

I am new to reinforcement learning. Lately, I have learned Q-learning using the following tutorial. Is Q-learning still possible if the environment is dynamic. Using the environment of the tutorial ...
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28 views

Questions on AlphaZero Implementation

So I've been implementing AlphaZero for Chess from scratch and there were a few things the papers mentioned that I'm not sure how to implement. I'll reference both the original AlphaGoZero paper and ...
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1answer
24 views

What is softmax in reinforcement learning?

There are multiple very complicated articles about softmax online. I just want to know a few things about it: Why do we need softmax? What is the "problem"? What does it do? How does it do ...
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64 views

Relationship between dynamic programming and reinforcement learning

I wasn't sure whether to post this here or in the ai stack exchange - please let me know if i need to move my post elsewhere) I have been learning about how dynamic programming can be used as a tool ...
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17 views

Is MDP Considered as The Model-based Value Iteration in/of Reinforcement Learning?

Is MDP Considered as The Model-based Value Iteration in/of Reinforcement Learning? If no, then Reinforcement Learning is all about being Model-free learning. Right?
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1answer
3k views

What is the difference between apprenticeship and imitation learning?

There are many sub-domains of reinforcement learning, two of which are apprenticeship and imitation learning - but that appears just to be two different names for the same thing?
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35 views

Is there a difference in the convergence analysis/proof of the chaotic learning automaton compared to the standard LA?

We have recently presented an article entitled Improving learning ability of learning automata using chaos theory. In this article, a new type of learning automaton called chaotic Learning Automaton (...
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19 views

Minimizing the length a Boolean Algebra Expression in disjunctive normal form

I'm looking to minimize the length of an expression in boolean algebra that has been given in disjunctive normal form and is free from redundancy. To remove redundancy from the original expression I ...
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1answer
35 views

What solution to apply for finding the optimal parameters?

For a study, I have a system (black-box) that requires an input in the form of an array with 4 values (input_array) and depending on their values it produces an ...
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2answers
33 views

Time efficient way to implement Multi-Armed-Bandits?

I'm doing a research on Multi-Armed Bandit (MAB) problem with approx. 1 million arms. In contrast, the number of iterations is of course much larger, about 10-20 million. Most MAB-algorithms require ...
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2answers
236 views

Why does the effectiveness of my reinforcement based neural network recede after a while?

I have a reinforcement based neural network training on the OpenAI gym CartPole-v1 environment. For the structure and training algorithm, assume it is the same as the one in this article. Typically, ...
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17 views

Path planning agent in a grid with compulsory states to visit with Reinforcement Learning

Is it possible to make a Reinforcement Learning agent for path planning in a 2D grid where visiting certain intermediate states is mandatory? Please give an insight if it is indeed possible as to ...
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1answer
42 views

What are the basics of CS i should know,before I start my journey into machine learning

I am myself a non-cs graduate and would love to be a machine learning engineer. I have learned to code and know the basics of <...
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29 views

Use ML to create a graph

I'm currently looking for literature/papers on machine learning techniques to create structures. In detail, I want to generate finite automata (NFA, DFA), which are useful for student-exercises. So I ...
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133 views

Converting Turing machine into the source code in industrial programming language?

Are there methods how to convert Turing machine (e.g. neural Turing machine or other rigorous Turing machine) into the source code/program that is written in some industrial programming language like ...
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23 views

Value flow (and economics) in stacked reinforcement learning systems: agent as reinforcement environment for other agents?

There is evolving notion of stacked reinforcement learning systems, e.g. https://www.ijcai.org/proceedings/2018/0103.pdf - where one RL systems executes actions of the second RL system and it itself ...
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7 views

Is there any intersection in the applications of multi-agent reinforcement learning and more traditional branches of machine learning?

From my limited understanding, it seems like the structure of problems that multi agent reinforcement learning attempts to attack is quite different from the structure of problems in more traditional ...
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29 views

Why is bias not defined for non-stationary MDP policies?

The Handbook of Markov Decision Processes 1 defines bias of a stationary deterministic policy as follows: $h(x, \phi) = \sum_{n = 0}^{\infty} E[r(x_n, \phi(x_n)) - w(x_n , \phi)]$ where $x$ is a ...
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1answer
674 views

Why do we use the log in gradient-based reinforcement algorithms?

I've been reading some papers on reinforcement learning. $$\Delta w=\frac{\partial ln\ p_w}{\partial w}r$$ I often see expressions, similar to the above one, where the weights (denoted by $w$) are ...
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86 views

Hessian in reinforcement learning

The Hessian of multi-layered network exhibits known behaviour at critical points as shown in [1]. The tools of random matrix theory allow [2] to deduce the asymptotic distribution of the eigenvalues ...
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155 views

Is MCTS an appropriate method for this problem size (large action/state space)?

I'm doing a research on a finite horizon decision problem with $t=1,\dots,40$ periods. In every time step $t$, the (only) agent has to chose an action $a(t) \in A(t)$, while the agent is in state $s(t)...
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30 views

Why is the objective in a multi-armed bandit problems to minimize (cumulative) regret?

In a multi-armed bandit, the objective is to maximize the expected cumulative reward. This objective is usually (equivalently?) stated in terms of expected cumulative regret. Question: Why not just ...
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17 views

How to compute the sample update error?

In the book Reinforcement Learning An Introduction,Chapter 8.5,there is an example that compares the efficiency of expected and sample updates: According to the author, "In this case, sample updates ...
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1answer
8k views

The meaning of discount factor on reinforcement learning

After reading of the google deepmind achievements on Atari's games, I am trying to understand the q-learning and q-networks, but I am little bit confused. The confusion arise in the concept of the ...
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1answer
341 views

Understanding On-policy First Visit Monte Carlo Control algorithm

I am going through the Monte Carlo methods, and it's going fine until now. However, I am actually studying the On-Policy First Visit Monte Carlo control for epsilon soft policies, which allows us to ...
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1answer
165 views

Help needed for understanding proof of No Regret Multi Armed Bandit Algorithm

I was reading Elad Hazan's book on Online Convex Optimization(http://ocobook.cs.princeton.edu/OCObook.pdf) and am facing difficulty understanding the proof given for the No regret algorithm for MAB (...
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2answers
112 views

Eligibility trace and the role of gamma and lambda

From R.Sutton's book, the eligibility trace update rule is: $$ E_t(s)\leftarrow\gamma~\lambda~ E_{t-1}(s)+\mathbb{1}(S_t=s) $$ I wonder why do we need both $\gamma$ and $\lambda$ to assign credit to ...
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0answers
42 views

What does actually happens on tile hashing

I am going through Richard Sutton book about Reinforcement Learning and I just encountered the tile coding method. I understood pretty well the principles, however, at the very end of the section, ...
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1answer
111 views

(DROP) Data Reduction Algorithm - How it works?

I am studing a PHD framework which the propose is to reduce the dataset with the most representative samples for training a classifier. Maybe I am loosing something, but I could not undestand a ...
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2answers
89 views

Is the credit assignment problem a well-posed one?

Credit assignment is the process of assigning credit (or blame) to a particular move in a sequence of moves (temporal credit assignment) or to a particular node (structural credit assignment) among ...
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1answer
398 views

Is deep learning appropriate to approximate dynamic programming problems?

I have a problem which can be completely solved using dynamic programming, but in a very intractable way (On^4, where n is around 1000). I won't get into the details of the problem since it's a bit ...
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3answers
470 views

Is TD-learning considered a model-based algorithm?

Differently from Sarsa and Q-learning, pure temporal difference learning (TD-learning) works with state value functions $V(s)$ and not state-action Q value functions $Q(s,a)$. It means that, in order ...
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1answer
161 views

Existence of Optimal Policy for infinite-state MDPs

It is well-know from Puterman's book (1994) that in any finite-state MDP, if there exists an optimal policy, then that policy is stationary and deterministic. How about MDPs with continuous state ...
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2answers
150 views

Reinforcement learning without self-play

Self-play seems to be very important for reinforcement learning algorithms. Can we train reinforcement learning algorithms without self-play (or learn mostly from past experiences)? For example, let'...
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0answers
62 views

Multi Arm Bandit (MAB) — Increasing reward function

In the general stochastic MAB model, the reward obtained at each trial is generally assumed to be independent of previous trials and obtained from some fixed (but unknown) distribution. However, if ...
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43 views

Q-Learning Error Bounds

I have searched a lot for this, but apparently there is no result on calculating any bound on the error $||Q-Q^*||$ when I stop Q-learning after say $N$ iterations ($Q$ is the vector of Q-values at ...