Questions tagged [reinforcement-learning]

For questions related to reinforcement learning, i.e. a machine learning technique where we imagine an agent that interacts with an environment (composed of states) in time steps by taking actions and receiving rewards (or reinforcements), then, based on these interactions, the agent tries to find a policy (i.e. a behavioural strategy) that maximizes the cumulative reward (in the long run), so the goal of the agent is to maximize the reward.

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37 views

Pytorch Deep q network not learning and step not stepping towards target

I am trying to create a simple deep q network for rl with conv2d layers. I can’t figure out what I am doing wrong, and the only thing I can see that doesn’t seem right is when I get the model ...
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29 views

What framework for a project with a custom environment?

I'm planning an RL project and I have to decide which RL framework do I use if any at all. The project has a highly custom environment, and testing different algorithms will be required to obtain ...
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38 views

What are some suitable positive functions as activations of neural networks?

I am working on a deep Q-learning project. My project is different than normal deep Q-learning. The rewards of my neural network must be positive because I need their values to importance sample ...
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52 views

Use of virtual worlds (e.g. Second Life) for training Artificial General Intelligence agents?

There is emerging effort for Third Wave Artificial Intelligence (Artificial General Intelligence) (http://hlc.doc.ic.ac.uk/3AI_HLC_2019.html and https://www.darpa.mil/work-with-us/ai-next-campaign) ...
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28 views

How to understand this NN architecture?

I was reading a paper Multi-Agent Reinforcement Learning for Adaptive User Association in Dynamic mmWave Networks and I was stuck understanding the deep neural network architecture that was used. The ...
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14 views

is it ok to take random actions while training a3c as in below code

i am trying to train an A3C algorithm but I am getting same output in the multinomial function. can I train the A3C with random actions as in below code. can someone expert comment. ...
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44 views

Why scaling reward drastically affects performance?

I have devised an gridworld-like environment where a RL agent is tasked to cover all the blank squares by passing through them. Possible actions are up, down, left, right. The reward scheme is the ...
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1answer
46 views

How is exponential moving average computed in deep Q networks?

In normal Q-learning, the update rule is an implementation of the exponential moving average, which then converges to the optimal true Q values. However, looking at DQN, how exactly is the exponential ...
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28 views

Using DDPG for control in multi-dimensional continuous action space?

I am relatively new to reinforcement learning, and I am trying to implement a reinforcement learning algorithm that can do continuous control in a custom environment. The state of the environment is ...
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28 views

Customized food for persons based on their profile using Reinforcement learning

I am newbie to Reinforcement Learning, this is my idea - Agent(food provider) has to select a food based on the environment(based on the user profile). Here the reward will be given to the agent based ...
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21 views

Trying to proof off policy TD Learning formula

I was reading the book "Introduction to Reinforcement Learning" by Richard Sutton In section 7.3 he write the formula for n-step off-policy TD as:. $$V(S_t) = V(S_{t-1}) + \alpha \rho_{t:t+n-...
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When past states contain useful information, does A3C perform better than TD3, given that TD3 does not use an LSTM?

I am trying to build an AI that needs to have some information about the past states as well. Therefore, LSTMs are suitable for this. Now, I want to know that for a problem/game like Breakout, where ...
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32 views

How can deep Q-learning converge if the targets may not be correct?

In deep Q-learning, $Q(s, a)$ and $Q'(s, a)$ are predicted or estimated by the neural network itself. In supervised learning, the target value is a true unbiased value. However, this isn't the case in ...
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32 views

What does self-play in reinforcement learning lead to?

Suppose, instead of playing against a random opponent, the reinforcement learning algorithm described above played against itself, with both sides learning. What do you think would happen in this case?...
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69 views

What happens if our target network overestimates the value?

When we use DDQN, we often use the target network in case our online network overestimates a value, but this doesn't make sense to me, because What happens if our target network is the one that ...
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55 views

What is the best way to make a deep reinforcement learning environment with a continuous 2D action space?

I understand that the actor-critic method is probably where I want to start because of how it works with continuous action spaces. However, the problem I am trying to solve would require the action be ...
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39 views

What is the difference between Bayes-adaptive MDP and a Belief-MDP in Reinforcement Learning?

I have been reading a few papers in this area recently and I keep coming across these two terms. As far as I'm aware, Belief-MDPs are when you cast a POMDP as a regular MDP with a continuous state ...
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71 views

What is the advantage of using experience replay (as opposed to feeding it sequential data)?

Let's suppose that our RL agent needs to play a game with different levels. If we train our RL agent sequentially or with sequential data, our agent will learn how to play level 1, but then it will ...
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52 views

When to apply reward for time series data?

Reading the paper 'Reinforcement Learning for FX trading 'at https://stanford.edu/class/msande448/2019/Final_reports/gr2.pdf it states: While our end goal is to be able to make decisions on a ...
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39 views

What is a multi channel supervised classifier?

I came across a paper that describes its model architecture in the following way. Our TRIL network is a two-channel network jointly trained to predict the expert’s action given state and the system’s ...
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47 views

Classification or regression for deep Q learning

DQN implemented at https://github.com/PacktPublishing/PyTorch-1.x-Reinforcement-Learning-Cookbook/blob/master/Chapter07/chapter7/dqn.py uses the mean square error loss function for the neural network ...
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53 views

In continuous action spaces, how is the standard deviation, associated with Gaussian distribution from which actions are sampled, represented?

I have a question about implementing policy gradient methods for problems with continuous action spaces. Assume that actions are sampled from a diagonal Gaussian distribution with mean vector $\mu$ ...
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35 views

How can I convert a simple CLI RPG to a compatible environment for training an RL agent via stable-baselines?

What would be the good choice of algorithm to use for character action selection in an RPG, implemented in Python? I had previously asked this question in the hopes of getting headway on the AI ...
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44 views

Correct dimensionality of parameter vector for solving an MRP with linear function approximation?

I'm in the process of trying to learn more about RL by shadowing a course offered collaboratively by UCL and DeepMind that has been made available to the public. I'm most of the way through the course,...
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32 views

Solving multi-armed bandit problems with continuous action space

My problem has a single state and an infinite amount of actions on a certain interval (0,1). After quite some time of googling I found a few paper about an algorithm called zooming algorithm which can ...
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23 views

Should the importance sampling ratio be updated at the end of the for loop in the off-policy Monte Carlo control algorithm?

I'm studying RL with Sutton and Barto's book. I'd like to ask about the order of execution of a statement in the algorithm below. Here, $W$ (importance sampling ratio) is updated at the end of the <...
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31 views

What is the role of embeddings in a deep recurrent Q network?

When describing the model architecture for a deep recurrent q network, the authors of the paper Learning to Communicate with Deep Multi-Agent Reinforcement Learning each agent consists of a recurrent ...
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1answer
57 views

Do we need multiple parallel environments to train in batches an on-policy algorithm?

When using an on-policy method in reinforcement learning, like advantage actor-critic, you shouldn't use old data from an experience buffer, since a new policy requires new data. Does this mean that ...
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25 views

Tic-tac-toe: How would standard SARSA and Q-learning yield different results in the agent's behaviour?

I know this is deceptively simple. Tic tac toe is a well studied game for RL. Assume your agent is playing aggainst a strong opponent. I know you deal in after states. I know that in Q learning the ...
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29 views

How are the coefficients of the Region of Interest being selected?

I was reading the following paper: Rl-Ncs: Reinforcement Learning Based Data-Driven Approach For Nonuniform Compressed Sensing, and my question is: how do they decide whether a signal is characterized ...
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52 views

Atari Games: Pretrained CNN to accelerate training?

DQN for Atari takes considerable training time. For example, the 2015 paper in Nature notes that algorithms are trained for 50 million frames or equivalently around 38 days of game experience in total....
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35 views

Off-Policy Estimation - Importance Sampling with Negative Rewards

Importance sampling is a common method for calculating off-policy estimates in RL. I have been reading through some of the original documentation (D.G. Horvitz and D.J. Thompson, Powell, M.J. and ...
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27 views

What is the proof that the variance of the gradient estimate in Actor-Critic is smaller than in REINFORCE?

The intuition provided when introducing actor-critic algorithms is that the variance of its gradient estimates is smaller than in REINFORCE as, e.g., discussed here. This intuition makes sense for the ...
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27 views

Why do bootstrapping methods produce nonstationary targets more than non-bootstrapping methods?

The following quote is taken from the beginning of the chapter on "Approximate Solution Methods" (p. 198) in "Reinforcement Learning" by Sutton & Barto (2018): reinforcement ...
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1answer
35 views

How to deal with the addition of a new state to the environment during training?

Let's say we have a dynamic environment: a new state gets added after 2000 episodes have been done. So, we leave room for exploration, so that it can discover the new state. When it gets to that new ...
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34 views

How to choose an RL algorithm for a Gridworld that models a much more complex problem

I am considering using Reinforcement Learning to do optimal control of a complex process that is controlled by two parameters $(n_O, n_I), \quad n_I = 1,2,3,\dots, M_I, n_O = 1,2,3,\dots, M_O$ In this ...
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26 views

Are policy-based methods better than value-based methods only for large action spaces?

In different books on reinforcement learning, policy-based methods are motivated by their ability to handle large (continuous) action spaces. Is this the only motivation for the policy-based methods? ...
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1answer
52 views

Continuous state and continuous action Markov decision process time complexity estimate: backward induction VS policy gradient method (RL)

Model Description: Model based(assume known of the entire model) Markov decision process. Time($t$): Finite horizon discrete time with discounting factor State($x_t$): Continuous multi-dimensional ...
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51 views

Is Reinforcement Learning what I need for this image to image translation problem?

I have a paired dataset of binary images A and B: A1 paired with B1, A2-B2, etc., with simple shapes (rectangles, squares). The external software receives both images A and B and it returns a number ...
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32 views

Is there a way to show convergence of DQN other than by eye observation?

I made a DQN model and plot its reward curve. You can see intuitively that the curve already converged since its reward value now just oscillates. How can I show confidence that my DQN already reached ...
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36 views

Is this figure a correct representation of off-policy actor-critic methods?

Does this figure correctly represent the overall general idea about actor-critic methods for on-policy (left) and off-policy (right) case? I am a bit confused about the off-policy case (right figure). ...
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48 views

Does the off-policy evaluation work for non-stationary policies?

As the title says, in reinforcement learning, does the off-policy evaluation work for non-stationary policies? For example, IS (importance sampling)-based estimators, such as weighted IS or doubly ...
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41 views

In layman's terms, what is stochastic computation graph?

I'm going through the distributions package on PyTorch's documentation and came across the term stochastic computation graph. In layman's terms, what is it?
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40 views

What would be the good choice of algorithm to use for character action selection in an RPG, implemented in Python?

I have developed an RPG in likeness to the features showcased in the Final Fantasy series; multiple character classes which utilise unique action sets, sequential turn-based combat, front/back row ...
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39 views

Designing a reward function for my reinforcement learning problem

I'm working on a project lately and I'm trying to solve a problem with reinforcement learning and I have serious issues with shaping the reward function. The problem is designing a device with maximum ...
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50 views

Can reinforcement learning algorithms be applied on problems involving a very large number of possible actions?

There is a question already about applying RL to "large scale problems", where large scale refers to the problem of a relatively small number of actions (that could be from a continous space) ...
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45 views

Why does the n-step return being zero result in high variance in off policy n-step TD?

In the paragraph given between eq 7.12 and 7.13 in Sutton & Barto's book: $G_{t:h} = R_{t+1} + G_{t+1:h} , t < h < T$ where $G_{h:h} = V_{h-1}(S_h)$. (Recall that this return is used at ...
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1answer
91 views

How to implement RAM versions of Atari games

I have coded the breakout RAM version, but, unfortunately, its highest reward was 5. I trained it for about 2 hours and never reached a higher score. The code is huge, so I can't paste here, but, in ...
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1answer
43 views

When discounted MAB is useful?

Many of multi-armed bandit algorithms are used when the total reward is the sum of all rewards. However, in RL, the discounted reward is mainly used. Why the discounted reward is not prevailing in MAB ...
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71 views

How can the target rely on untrained parameters?

I'm trying to understand DQN. I understand where the loss function comes from. I'm just unsure about why the target function works in practice. Given the loss function $$ L_i(\theta_i) = [(y_i - Q(s,a;...

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