Questions tagged [reinforcement-learning]

The tag has no usage guidance.

42 questions with no upvoted or accepted answers
Filter by
Sorted by
Tagged with
6
votes
1answer
541 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 ...
4
votes
1answer
292 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 ...
3
votes
0answers
533 views

Reinforcement learning - state space and action space

I am working on a reinforcement learning strategy for parameter control of a local search heuristic. The complete state for this RL problem can be defined as $S = \{s, p\}$, where $s$ and $p$ ...
2
votes
0answers
20 views

Where to find the current state of the art performance of Deep RL algorithms?

Recently, I had an idea of a novel Deep RL algorithm that might perform better than existing algorithms such as DQN, TRPO, PPO, etc. However, I do not know of a website or a research paper that might ...
2
votes
0answers
150 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 ...
2
votes
1answer
58 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 ...
2
votes
0answers
129 views

Q-learned policy differs from double-Q-learned policy

I implemented Q-learning and double-Q-learning as presented in Sutton's "Reinforcement Learning: An Introduction". I test the algorithms on the OpenAI cliff walking gym and analyze the resulting ...
2
votes
0answers
66 views

How to best model multidimensional, continuous, non-convex "shape" as neural network?

I have: set of n-dimensional points that I know are inside of the shape n >= 18, range on all dimensions has upper bound and lower bound (no axis goes to infinity). shape is pretty large in this n-...
1
vote
0answers
9 views

Evaluating the safety of deep RL algorithms

Is there any free/open-source environment, tasks, or dataset for evaluating deep RL algorithms in terms of safety? all available environments (like openAI's) are environments for simple games. These ...
1
vote
0answers
27 views

Deep RL for healthcare: existing benchmarking datasets or environments

I am currently a Ph.D. student in the computer science department, I was given the subject of Deep RL for Healthcare. However, after lots of research on the internet, I could not find any free dataset ...
1
vote
0answers
10 views

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 ...
1
vote
0answers
22 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(...
1
vote
0answers
32 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 ...
1
vote
0answers
16 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 ...
1
vote
0answers
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 ...
1
vote
0answers
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 ...
1
vote
0answers
165 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)...
1
vote
0answers
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 ...
1
vote
0answers
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 ...
1
vote
0answers
43 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, ...
1
vote
0answers
67 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 ...
1
vote
0answers
29 views

Markov Decision Process Optimal Policy

Consider the setting of finite MDPs. I will be using the notation in Chapter 2 of http://rll.berkeley.edu/deeprlcourse/docs/ng-thesis.pdf. Say we have already computed values for the optimal $Q$-...
1
vote
0answers
73 views

Policy Gradient Methods

would be really thankful if someone could clear this up. In reinforcement learning, When we use basis like the fourier or say polynomial basis, and say we have 4 actions, theta will then be a N x4 ...
1
vote
0answers
80 views

Inconsistent results with Q-learning

I'm running a q-learning agent in tic tac toe against a minimax agent and measuring the tie rate (since you can never win against minimax). When I run 10,000 training games, I've found that there seem ...
1
vote
0answers
41 views

Middle ground between model-based and model-free approaches

In the context of the reinforcement learning domain, the dichotomy between model-based (learn a model and used it to determine a controller) and model-free (learn a controller without learning a model)...
0
votes
0answers
16 views

The expected total reward of an Epsilon-Greedy algorithm

I have acknowledged that the $\epsilon$-greedy algorithm does exploration and exploitation with probability $1-\epsilon$ and $\epsilon$. Is there a way to simply express the expected total reward $\...
0
votes
0answers
36 views

Ranking messages on social media

People are always talking about "the algorithm" for social media platforms like Facebook and Twitter but I haven't yet seen a good explanation of what exactly these algorithms are actually ...
0
votes
0answers
9 views

Resource Allocation problem using Deep Reinforcement Learning

I am working on a resource allocation problem, and I need some help. I have a few Jobs (J) and few Machines (M) to complete them. Each job has a size (s_j), resource demand (c_j), and a delay bound (...
0
votes
0answers
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 ...
0
votes
1answer
31 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 ...
0
votes
0answers
21 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 ...
0
votes
0answers
35 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 ...
0
votes
0answers
18 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?
0
votes
1answer
146 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 ...
0
votes
0answers
21 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 ...
0
votes
0answers
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 ...
0
votes
0answers
30 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 ...
0
votes
0answers
162 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 ...
0
votes
0answers
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 ...
0
votes
0answers
93 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 ...
0
votes
0answers
44 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 ...
0
votes
0answers
277 views

How are punishment and negative reinforcement handled in reinforcement learning using neural net function approximation?

Punishment is reducing a behaviour due to bad outcome e.g. A cow stops touching an electric fence because it gets a shock. Negative reinforcement is increasing a behaviour that reduces a bad outcome. ...