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Reinforcement Learning DDPG: creating a meaningful heat map of action value and state

Algorithm: Double Deep Policy Gradient Output of Actor-Network: Mean action value and variance Observation (states): x,y coords My goal is to create a heat map that has a meaningful representation of ...
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Calculating sequence of $\lambda$ returns

I am having some trouble with answering the following question: A rat is involved in an experiment. It experiences one episode. At the first step it hears a bell. At the second step it sees a light. ...
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Main subjects to learn Artificial Intelligence in CS

In my PhD, I will work with ML models. However, I will only use ready-made models as a tool, but I want to delve deeper into Artificial Intelligence not just to use ready-made models, but to ...
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How to distinguish between Unrepresentative Train Dataset and minimal overfitting from a learning c

Is this a minimal overfitting or like unpresensitive train dataset? However, I read it on the net but still have a problem with the graph! And then I read the following text on the net and again I ...
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DeepMind Alphadev: How did it use Reinforcement Learning to reduce the search space?

Google DeepMind recently published a new paper which describes how they used a reinforcement learning to discover faster sorting algorithms. A summary of the paper is here and the paper is here. It ...
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Multi-Armed Bandit - Reward Probabilities

I am new to reinforcement learning, and recently came across the following issue. When implementing a multi-armed bandit algorithm, we assume we have k machines with reward probabilities [p_1,..., p_k]...
alextan's user avatar
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Tabular Meta-Learning in RL

There are various meta-learning algorithms in RL that are proposed for settings when we have a (deep) neural network and the policy (or the value function) are parameterized as such. Can these methods ...
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Reinforcement Learning Reward Function for Optimizing Golf Aim?

I read this article, mentioning that either here, or StackOverflow would be the best places to ask generic machine learning questions, however, if the question isn't programming specific with a ...
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Long and short memory in reinforcement learning Connect 4 AI

I'm writing an AI based on reinforcement learning to play Connect 4. That's my second bot and attempt to RNN and AI (first was copy a code of snake RNN AI from youtube) and I'm looking for some ...
Saguro's user avatar
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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 ...
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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 ...
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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 ...
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What makes Deep RL "fundamentally/mathematically" advantageous?

Note: I consider myself to be a beginner in the field of Deep RL. Deep RL has proven tremendous success in recent years like playing atari and beating go champion. Therefore, considerable interest for ...
mac179's user avatar
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How to setup the Bellman Equation as a linear system of equation

I was watching a video on Reinforcement Learning by Andrew Ng, and at about minute 23 of the video he mentions that we can represent the Bellman equation as a linear system of equations. I am talking ...
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What is the relation between the compatibile features and the state features in Actor Critic Algorithm?

According to Actor-Critic algorithm, $\psi_{\theta}=\nabla_{\theta}\ln \mu_{\theta}(s, a)$ where $\mu_{\theta}(s, a)$ is the policy followed by the actor and $\psi_\theta$ is the compatibile features ...
Roshan Jacob's user avatar
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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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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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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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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 ...
mnijhum's user avatar
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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(...
farhanhubble's user avatar
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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 ...
Diego Benalcázar's user avatar
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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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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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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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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 ...
Micha Christ's user avatar
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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 ...
quest ions's user avatar
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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 ...
Kuroi Ryū's user avatar
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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 ...
Aki's user avatar
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3 votes
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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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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 ...
Fergus Kavanagh's user avatar
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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 ...
Cristian M's user avatar
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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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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 ...
fearKing's user avatar
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1 answer
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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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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 ...
Timo Bergerbusch's user avatar
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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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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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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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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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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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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 ...
jonem's user avatar
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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 ...
z4z5's user avatar
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1 answer
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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 (...
csTheoryBeginner's user avatar
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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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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 ...
naifmeh's user avatar
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(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 ...
rej's user avatar
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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 ...
Joebevo's user avatar
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1 vote
1 answer
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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 ...
lightning's user avatar
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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 ...
user88903's user avatar
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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 ...
Perissiane's user avatar