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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43
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6answers
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What's the difference between model-free and model-based reinforcement learning?

What's the difference between model-free and model-based reinforcement learning? It seems to me that any model-free learner, learning through trial and error, could be reframed as model-based. In ...
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4answers
8k views

How to handle invalid moves in reinforcement learning?

I want to create an AI which can play five-in-a-row/gomoku. As I mentioned in the title, I want to use reinforcement learning for this. I use policy gradient method, namely REINFORCE, with baseline. ...
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2answers
7k views

What is the relation between Q-learning and policy gradients methods?

As far as I understand, Q-learning and policy gradients (PG) are the two major approaches used to solve RL problems. While Q-learning aims to predict the reward of a certain action taken in a certain ...
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2answers
6k views

What is sample efficiency, and how can importance sampling be used to achieve it?

For instance, the title of this paper reads: "Sample Efficient Actor-Critic with Experience Replay". What is sample efficiency, and how can importance sampling be used to achieve it?
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2answers
7k views

How to define states in reinforcement learning?

I am studying reinforcement learning and the variants of it. I am starting to get an understanding of how the algorithms work and how they apply to an MDP. What I don't understand is the process of ...
14
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3answers
842 views

Why doesn't Q-learning converge when using function approximation?

The tabular Q-learning algorithm is guaranteed to find the optimal $Q$ function, $Q^*$, provided the following conditions (the Robbins-Monro conditions) regarding the learning rate are satisfied $\...
14
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1answer
9k views

How does LSTM in deep reinforcement learning differ from experience replay?

In the paper Deep Recurrent Q-Learning for Partially Observable MDPs, the author processed the Atari game frames with an LSTM layer at the end. My questions are: How does this method differ from the ...
14
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2answers
4k views

Inconsistent action space in Reinforcement Learning

This question is regarding Reinforcement Learning and different/inconsistent action space for every/some states. What do I mean by inconsistent action space? Let say you have an MDP where the number ...
13
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3answers
1k views

Why does the discount rate in the REINFORCE algorithm appear twice?

I was reading the book Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto (complete draft, November 5, 2017). On page 271, the pseudo-code for the episodic Monte-Carlo ...
13
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1answer
2k views

Why do you not see dropout layers on reinforcement learning examples?

I've been looking at reinforcement learning, and specifically playing around with creating my own environments to use with the OpenAI Gym AI. I am using agents from the stable_baselines project to ...
13
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3answers
3k views

How to implement a constrained action space in reinforcement learning?

I'm coding a reinforcement learning model with a PPO agent thanks to the very good Tensorforce library, built on top of Tensorflow. The first version was very simple and I'm now diving into a more ...
13
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1answer
2k views

When should I use Reinforcement Learning vs PID Control?

When designing solutions to problems such as the Lunar Lander on OpenAIGym, Reinforcement Learning is a tempting means of giving the agent adequate action control so as to successfully land. But ...
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3answers
7k views

Are there any applications of reinforcement learning other than games?

Is there a way to teach reinforcement learning in applications other than games? The only examples I can find on the Internet are of game agents. I understand that VNC's control the input to the ...
12
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1answer
2k views

Why does DQN require two different networks?

I was going through this implementation of DQN and I see that on line 124 and 125 two different Q networks have been initialized. From my understanding, I think one network predicts the appropriate ...
11
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3answers
5k views

What is the difference between actor-critic and advantage actor-critic?

I'm struggling to understand the difference between actor-critic and advantage actor-critic. At least, I know they are different from asynchronous advantage actor-critic (A3C), as A3C adds an ...
11
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1answer
367 views

How to stay a up-to-date researcher in ML/RL community?

As a student who wants to work on machine learning, I would like to know how it is possible to start my studies and how to follow it to stay up-to-date. For example, I am willing to work on RL and MAB ...
11
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1answer
6k views

How can policy gradients be applied in the case of multiple continuous actions?

Trusted Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO) are two cutting edge policy gradients algorithms. When using a single continuous action, normally, you would use some ...
10
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1answer
1k views

Why does the policy network in AlphaZero work?

In AlphaZero, the policy network (or head of the network) maps game states to a distribution of the likelihood of taking each action. This distribution covers all possible actions from that state. ...
10
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1answer
3k views

What is the Bellman operator in reinforcement learning?

In mathematics, the word operator can refer to several distinct but related concepts. An operator can be defined as a function between two vector spaces, it can be defined as a function where the ...
10
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3answers
1k views

Is the optimal policy always stochastic if the environment is also stochastic?

Is the optimal policy always stochastic (that is, a map from states to a probability distribution over actions) if the environment is also stochastic? Intuitively, if the environment is ...
10
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4answers
1k views

What does “stationary” mean in the context of reinforcement learning?

I think I've seen the expressions "stationary data", "stationary dynamics" and "stationary policy", among others, in the context of reinforcement learning. What does it mean? I think stationary policy ...
10
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4answers
485 views

Can a neural network work out the concept of distance?

Imagine a game where it is a black screen apart from a red pixel and a blue pixel. Given this game to a human, they will first see that pressing the arrow keys will move the red pixel. The next thing ...
9
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3answers
635 views

Does Monte Carlo tree search qualify as machine learning?

To the best of my understanding, the Monte Carlo tree search (MCTS) algorithm is an alternative to minimax for searching a tree of nodes. It works by choosing a move (generally, the one with the ...
9
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3answers
2k views

What is a “trajectory” in reinforcement learning?

I'm now learning about reinforcement learning, but I just found the word "trajectory" in this answer. However, I'm not sure what it means. I read a few books on the Reinforcement Learning but none of ...
9
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1answer
4k views

What is the difference between an observation and a state in reinforcement learning?

I'm studying reinforcement learning. It seems that "state" and "observation" mean exactly the same thing. They both capture the current state of the game. Is there a difference between the two terms?...
9
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2answers
702 views

Why is baseline conditional on state at some timestep unbiased?

In the homework for the Berkeley RL class, problem 1, it asks you to show that the policy gradient is still unbiased if the baseline subtracted is a function of the state at time step $t$. $$ \...
9
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1answer
2k views

A few doubts regarding the application of reinforcement learning to games like chess

I invented a chess-like board game. I built an engine so that it can play autonomously. The engine is basically a decision tree. It's composed by: A search function that at each node finds all ...
9
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1answer
2k views

What is the difference between expected return and value function?

I've seen numerous mathematical explanations of reward, value functions $V(s)$, and return functions. The reward provides an immediate return for being in a specific state. The better the reward, the ...
9
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1answer
2k views

Do off-policy policy gradient methods exist?

Do off-policy policy gradient methods exist? I know that policy gradient methods themselves using the policy function for sampling rollouts. But can't we easily have a model for sampling from the ...
8
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5answers
429 views

What's a good resource for getting familiar with reinforcement learning?

I am familiar with supervised and unsupervised learning. I did the SaaS course done by Andrew Ng on Coursera.org. I am looking for something similar for reinforcement learning. Can you recommend ...
8
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1answer
4k views

What is the credit assignment problem?

In reinforcement learning (RL), the credit assignment problem (CAP) seems to be an important problem. What is the CAP? Why is it relevant to RL?
8
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3answers
12k views

What are different actions in action space of environment of 'Pong-v0' game from openai gym?

Printing actionspace for Pong-v0 gives 'Discrete(6)' as output, i.e.0,1,2,3,4,5 are actions defined in environment as per documentation, but game needs only two controls. Why this discrepency? Further ...
8
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3answers
3k views

Why does is make sense to normalize rewards per episode in reinforcement learning?

In Open AI's actor-critic and in Open AI's REINFORCE, the rewards are being normalized like so rewards = (rewards - rewards.mean()) / (rewards.std() + eps) on ...
8
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1answer
530 views

Why is reinforcement learning not the answer to AGI?

I previously asked a question about How can an AI freely make decisions on a network?. I got a great answer about how current algorithms lack agency. The first thing I thought of was reinforcement ...
8
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2answers
844 views

How do we prove the n-step return error reduction property?

In section 7.1 (about the n-step bootstrapping) of the book Reinforcement Learning: An Introduction (2nd edition), by Andrew Barto and Richard S. Sutton, the authors write about what they call the "n-...
8
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1answer
169 views

Are Q-learning and SARSA the same when action selection is greedy?

I'm currently studying reinforcement learning and I'm having difficulties with question 6.12 in Sutton and Barto's book. Suppose action selection is greedy. Is Q-learning then exactly the same ...
8
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1answer
165 views

Are there reinforcement learning algorithms that scale to large problems?

Given a large problem, value iteration and other table based approaches seem to require too many iterations before they start to converge. Are there other reinforcement learning approaches that better ...
8
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1answer
3k views

What does the agent in reinforcement learning exactly do?

What is an agent in reinforcement learning (RL)? I think it is not the neural network behind. What does the agent in RL exactly do?
8
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1answer
239 views

Are there any other machine learning models apart from Reinforcement Learning and Q Learning to play video games?

OpenAI's Universe utilises RL algorithms and I have heard of some game-training projects using Q learning, but are there any others which are used to master/win games? Can genetic algorithms be used ...
8
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1answer
295 views

What are some resources on continuous state and action spaces MDPs for reinforcement learning?

Most introductions to the field of MDPs and Reinforcement learning focus exclusively on domains where space and action variables are integers (and finite). This way we are introduced quickly to Value ...
8
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2answers
115 views

How can alpha zero learn if the tree search stops and restarts before finishing a game?

I am trying to understand how alpha zero works, but there is one point that I have problems understanding, even after reading several different explanations. As I understand it (see for example https:/...
8
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3answers
2k views

Huge action space size in Reinforcement Learning

I am working on creating a RL based AI for a certain board game. Just as a general overview of the game so that you understand what it's all about: It's a discrete turn-based game with a board of size ...
8
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2answers
554 views

Reinforcement Learning with asynchronous feedback

I want suggestions on literature on Reinforcement Learning algorithms that perform well with asynchronous feedback from the environment. What I mean by asynchronous ...
7
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2answers
1k views

Why exactly do neural networks require i.i.d. data?

In reinforcement learning, in general, successive states (actions and rewards) are highly correlated. An "experience replay" buffer was used, in the DQN architecture, to avoid training the neural ...
7
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2answers
1k views

How do I create an AI for a two-players board game?

Goal I want to create an artificial intelligence to compete against other players in a board game. Game explanation I have a board game similar to 'snakes and ladders'. You have to get to a final ...
7
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2answers
4k views

What is the difference between First-Visit Monte-Carlo and Every-Visit Monte-Carlo Policy Evaluation?

I came across this 2 algorithms but I cannot understand the difference between these 2 both in terms of implementation as well as intuitionally. So what difference does the second point in both the ...
7
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4answers
782 views

Are there any online competitions for Reinforcement Learning?

Kaggle is limited to only supervised learning problems. There used to be www.rl-competition.org but they've stopped. Is there anything else I can do other than locally trying out different algorithms ...
7
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1answer
203 views

Is Experience Replay like dreaming?

Drawing parallels between Machine Learning techniques and a human brain is a dangerous operation. When it is done successfully, it can be a powerful tool for vulgarisation, but when it is done with no ...
7
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1answer
122 views

What is the purpose of the actor in actor-critic algorithms?

For discrete action spaces, what is the purpose of the actor in actor-critic algorithms? My current understanding is that the critic estimates the future reward given an action, so why not just take ...
7
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1answer
3k views

What is the difference between reinforcement learning and optimal control?

Coming from a process (optimal) control background, I have begun studying the field of deep reinforcement learning. Sutton & Barto (2015) state that particularly important (to the writing of ...

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