Questions tagged [reinforcement-learning]

For questions related to learning controlled by external positive reinforcement or negative feedback signal or both, where learning and use of what has been thus far learned occur concurrently.

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How to integrate Dict space of OpenAI gym into a reinforcement learning framework?

I am implementing a gym environment and I have several input arrays as my input (different sizes). The most simple method to integrate my environment into the gym is to use the Dict space as my ...
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How is the state-visitation frequency computed in “Maximum Entropy Inverse Reinforcement Learning”?

I am trying to understand the formulation of the maximum entropy Inverse RL method by Brian Ziebart. Particularly, I am stuck on how to understand the computation of state - visitation frequencies. ...
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Equivalence between expected parameter increments in “Off-Policy Temporal-Difference Learning with Function Approximation”

I am having a hard time understanding the proof of theorem 1 presented in the "Off-Policy Temporal-Difference Learning with Function Approximation" paper. Let $\Delta \theta$ and $\Delta \bar{\theta}...
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10 views

Running a simple graph network example in gym

This a fix example to run in gym open ai ...
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24 views

How was the DQN trained to play many games?

Some people claim that DQN was used to play many Atari games. But what actually happened? Was DQN trained only once (with some data from all games) or was it trained separately for each game? What was ...
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1answer
86 views

In RL, if I assign the rewards for better positional play, the algorithm is learning nothing?

I'm creating an RL application for the game Connect Four. If I tell the algorithm which moves/token positions will receive greater rewards, surely it's not actually learning anything; it's just a ...
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+50

How to formulate normalization/probability conditions on state-action spaces in Gym?

I intend to develop a custom environment for open-ai's gym. My goal is for an agent to learn (among additional objectives) dividing a certain quantity drawn from a continous action space (i.e. spaces....
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What kind of artificial intelligence is this? A decentralized swarm intelligence where the input and output is split among the agents

I have an AI design for deciding the length of green and red lamps of the traffic. In my design, every crossroads has its own agent. This agent has input the amount of vehicle in each road in a single ...
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10 views

DQN is unable to learn from image data

I am trying to write a DQN model that will be able to solve OpenAI gym CartPole environment. I successfully managed to do it using scalar observation data that ...
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1answer
25 views

What is the relationship between the reward function and the value function?

To clarify it in my head, the value function calculates how 'good' it is to be in a certain state by summing all future (discounted) rewards, while the reward function is what the value function uses ...
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1answer
37 views

Should I be trying to create a generic or specific (to particular game) reinforcement learning agent?

I'm creating an RL application for the game Connect Four. In general, should I be aiming to create an application that's more generic, which would 'learn' different games, or specific to a ...
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How should I define the reward function for the Connect Four game?

I'm creating an RL application for the game Connect Four. I've researched the different strategies for the game and which positions are more favourable to lead to a win. Should I be assigning ...
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25 views

Why can the core reinforcement learning algorithms be applied to POMDPs?

Why can an AI, like AlphaStar, work in StarCraft, although the environment is only partially observable? As far as I know, there are no theoretical results on RL in the POMDP environment, but it ...
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2answers
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Why is there an expectation sign in the Bellman equation?

In chapter 3.5 of Sutton's book, the value function is defined as: Can someone give me some clarification about why there is the expectation sign behind the entire equation? Considering that the ...
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How can I find the appropriate reward value for my reinforcement learning problem?

I am wondering how can I find the appropriate reward value for each specific problem. I know this is a highly empirical process, but I am sure that the value is not set totally at random. I want to ...
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Is the TD-residual defined for timesteps $t$ past the length of the episode?

Let $\mathcal{S}$ be the state-space in a reinforcement learning problem where rewards are in $\mathbb{R}$, and let $V:\mathcal{S} \to \mathbb{R}$ be an approximate value function. Following the GAE ...
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2answers
38 views

Should I always start from the same start state in reinforcement learning?

In an episodic training of an RL agent, should I always start from the same initial state or I can start from several valid initial states? For example, in a gym environment, should my ...
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+100

Efficient algorithm to obtain near optimal policies for an MDP

Given a discrete, finite Markov Decision Process (MDP) with its usual parameters $(S, A, T, R, \gamma)$, it is possible to obtain the optimal policy $\pi^{*}$ and the optimal value function $V^{*}$ ...
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What are the most common non-Markov RL paradigms?

I am interested in doing model-free RL but not using the Markov assumptions typical for MDPs or POMDPs. What are alternative paradigms that don't rely on the Markov assumptions? Are there any common ...
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How should I define the loss function when using DQN to estimate the probability density?

I'm doing a Deep Q-learning project. All of my rewards are positive and there are two terminal states. One of them has a zero reward and the other has a high positive reward. The rewards are ...
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20 views

Is the MDP value function used in the minimax algorithm? [closed]

Is the MDP value function represented (or used) in the minimax algorithm? If so, could you please give an example?
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1answer
30 views

Can this be a possible deep q learning pseudocode?

I am not using replay here. Can this be a possible deep q learning pseudocode? ...
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21 views

How to define observation and action space for an array-like input?

I am working on a problem, and I want to implement it as a reinforcement learning problem and integrate it into the OpenAI's gym. My states are in the form of lists of length $n$, where each element ...
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7 views

How can I add logic for invalid moves when using stable-baselines in OpenAI's gym?

I want to integrate my environment into the OpenAI's gym and then use the stable baselines library for training it. The learning method in the stable baseline is with one-line learning and you don't ...
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How should I avoid illegal states in OpenAI's gym?

I'm trying to make a gym environment for a simulation problem. In my gym environment, I have a set of illegal states which I don't want my agent to go into them. What is the easiest way to add such ...
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21 views

How much time does it take to train DQN on Atari environment?

I am trying to build a DQN model for the Atari Pong game, but I am not sure whether the model is learning at all. I am using the architecture described in the paper Playing Atari with Deep ...
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27 views

Atari Breakout Infrastructure

This is how they describe their infrastructure in https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf. I want to implement the game of Atari Breakout. ...
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1answer
84 views

Which deep reinforcement learning algorithm is appropriate for my problem?

My task is to solve an optimization problem with deep reinforcement learning. I read about several algorithms like DQN, PPO, DDPG, and A2C/A3C but use cases always seem to be problems like video games ...
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1answer
22 views

Non-Neural Network algorithms for large state space in zero sum games

I was reading online that Tic Tac Toe has a state space of 3^9 = 19,683. From my basic understanding, this sounds too large to use with Q Learning, as the Q table would be huge? If that is the case, ...
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1answer
29 views

What does the notation ${s'\sim T(s,a,\cdot)}$ mean?

I have been seeing notations on Expectations with their respective subscripts such as $E_{s_0 \sim D}[V^\pi (s_0)] = \Sigma_{t=0}^\infty[\gamma^t\phi(s_t)]$. This equation is taken from https://ai....
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1answer
69 views

Is there any programming language designed by deep learning?

i mean Programming Language Implementation by Deep Learning / AI. I want to know this kind of information, any links and news are very welcome. sure AI drawing picture and design PCB, so why not.
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1answer
27 views

How can I develop a reinforcement learning agent that plays memory cards game?

I am new to RL, and I am thinking of doing a little project. The goal of the project is to learn an agent play the memory game with cards. I already created the program for detecting the cards on the ...
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What are the pros and cons of deep learning and machine learning to develop a trading system?

As I want to start coding a new Trading AI in this year (first based on Python and later maybe in C++) I stumbled over the following question: Today, I would like to make a pro/contra list with you ...
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Whats the correct loss function to use during deep Q-learning (discrete action space)

After playing around with normal Q-learning I have decided to switch to deep Q-learning and I have encountered this problem. As I understand, for a task with discrete action space, where there are 4 ...
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1answer
42 views

What does a joint probability density function have to do with Stochastic Optimal Control and Reinforcement Learning?

I stumbled upon a job offer from a company that was looking for someone who was good with Reinforcement Learning (applied to finance) and something in their offer caught my eye. It goes something like ...
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1answer
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Do all expert trajectories have the same starting state in apprenticeship learning?

In the apprenticeship learning algorithm described by Ng et al. in Apprenticeship Learning via Inverse Reinforcement Learning, they mention that expert trajectories come in the form of $\{s_0^i, s_1^i\...
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24 views

Why is my DQN model not getting better?

I've created a deep Q network. My model does not get better, and can't see what I'm doing wrong. I'm new to RL. Replay Memory ...
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16 views

Are there known error bounds for TD(0) with a constant learning rate?

Is there any known error bounds for the TD(0) algorithm for the value function after a finite number of iterations? $$ \Delta_t=\max_{s \in \mathcal{S}}|v_t(s)-v_\pi(s)|$$ $$v_{t+1}(s_t)=v_t(s_t)+\...
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2answers
74 views

What are some online courses for deep reinforcement learning?

What are some (good) online courses for deep reinforcement learning? I would like the course to be both programming and theoretical. I really liked David Silver's course, but the course dates from ...
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27 views

Representation of state space, action space and reward system for Reinforcement Learning problem

I am trying to solve the problem of an agent dynamically discovering(start with no information about the environment) the environment and to explore as much of the environment as possible without ...
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1answer
30 views

How to set the target for the actor in A2C?

I did a simple Actor-Critic implementation in Keras using 2 networks where the critic learns the Q-Values of every action, and the actor predicts probabilities for choosing each action. In training, ...
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17 views

Understanding log_prob for Normal distribution in pytorch [migrated]

I'm currently trying to solve Pendulum-v0 from the openAi gym environment which has a continuous action space. As a result, I need to use a Normal Distribution to sample my actions. What I don't ...
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38 views

Why haven't we solved the problem of bipedal walking?

This has been a mystery to me. All the walking robots look like idiots now. But we do have a lot of simulation-based results (Flexible Muscle-Based Locomotion for Bipedal Creatures ), so why can't we ...
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1answer
43 views

What is the relationship between the Q and V functions?

Suppose we have a policy $\pi$ and we use SARSA to evaluate $Q^\pi(s, a)$, where $a$ is the policy $\pi$. Can we say that $Q^\pi(s, a) = V^\pi(s)$? The reason why I think this can be the case is ...
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What are the guidelines for defining a reward function in reinforcement learning (bandit problem)?

I'm working currently on a problem and I'm using RL (bandit problem). In my system, I have an agent that chooses an action among $k$ possible actions, and a user that decides whether the agent ...
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36 views

Policy Gradient Reward Oscillation in MATLAB

I'm trying to train a Policy Gradient Agent with Baseline for my RL research. I'm using the in-built RL toolbox from MATLAB (https://www.mathworks.com/help/reinforcement-learning/ug/pg-agents.html) ...
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41 views

How to correctly implement self-play with DQN?

I have an environment where an agent faces an equal opponent, and while I've achieved OK performance implementing DQN and treating the opponent as a part of the environment, I think performance would ...
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34 views

How to calculate the advantage in policy gradient functions?

From my understanding of the REINFORCE policy gradient method, we gently nudge the probabilities of actions based on the advantages. More specifically, the positive advantages increase the ...
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23 views

How to apply hyperparameter optimization on Monte Carlo Tree Search?

I have a basic MCTS agent for the game of Hex (a turn based game). I want to tune the parameters of UCT (the Cp parameter) and the number of rollouts parameter. Where do I have to begin? The problem ...
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2answers
58 views

Why are reinforcement learning methods sample inefficient?

Reinforcement learning methods are considered to be extremely sample ineffcient. For example, in a recent Deepmind paper by Hessel et al, they showed that in order to reach human level performance on ...

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