Questions tagged [q-learning]

For questions related to the Q-learning algorithm, which is a model-free and temporal-difference reinforcement learning algorithm that attempts to approximate the Q function, which is a function that, given a state s and an action a, returns a real number that represents the return (or value) of state s when action a is taken from s. Q-learning was introduced in the PhD thesis "Learning from Delayed Rewards" (1989) by Watkins.

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17
votes
3answers
2k 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
votes
1answer
3k 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 ...
5
votes
2answers
297 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 ...
3
votes
1answer
356 views

How do we compute the target value when the agent ends up in the terminal state?

I am working on a deep reinforcement learning problem. Throughout the episode, there is a small positive and negative reward for good or bad decisions. In the end, there is a huge reward for the ...
32
votes
2answers
12k 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 ...
11
votes
2answers
4k views

Can Q-learning be used for continuous (state or action) spaces?

Many examples work with a table-based method for Q-learning. This may be suitable for a discrete state (observation) or action space, like a robot in a grid world, but is there a way to use Q-learning ...
5
votes
1answer
499 views

Is the discount not needed in a deterministic environment for Reinforcement Learning?

I'm now reading a book titled as "Deep Reinforcement Learning Hands-On" and the author said the following on the chapter about AlphaGo Zero: Self-play In AlphaGo Zero, the NN is used to ...
2
votes
1answer
1k views

Why are Q values updated according to the greedy policy?

Apparently, in the Q-learning algorithm, the Q values are not updated according to the "current policy", but according to a "greedy policy". Why is that the case? I think this is related to the fact ...
6
votes
1answer
2k views

Can Q-learning be used in a POMDP?

Can Q-learning (and SARSA) be directly used in a Partially Observable Markov Decision Process (POMDP)? If not, why not? My intuition is that the policies learned will be terrible because of partial ...
11
votes
2answers
1k 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-...
10
votes
1answer
578 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 ...
5
votes
1answer
1k views

Why is the target $r + \gamma \max_{a'} Q(s', a'; \theta_i^-)$ in the loss function of the DQN architecture?

In the paper Human-level control through deep reinforcement learning, the DQN architecture is presented, where the loss function is as follows $$ L_i(\theta_i) = \mathbb{E}_{(s, a, r, s') \sim U(D)} \...
3
votes
1answer
226 views

Why we don't use importance sampling in tabular Q-Learning?

Why don't we use an importance sampling ratio in Q-Learning, even though Q-Learning is an off-policy method? Importance sampling is used to calculate expectation of a random variable by using data ...
1
vote
1answer
2k views

What is the difference between the $\epsilon$-greedy and softmax policies?

Could someone explain to me which is the key difference between the $\epsilon$-greedy policy and the softmax policy? In particular, in the contest of SARSA and Q-Learning algorithms. I understood the ...
8
votes
1answer
2k views

What are other ways of handling invalid actions in scenarios where all rewards are either 0 (best reward) or negative?

I created an OpenAI Gym environment, and I would like to check the performance of the agent from OpenAI Baselines DQN approach on it. In my environment, the best possible outcome for the agent is 0 - ...
2
votes
1answer
220 views

Concrete Example for Q Learning

I am not sure if I understood the q learning algorithms correctly. Therefore I would give a concrete example and ask if someone can tell me how to update the q value correctly. First I initialized ...
1
vote
1answer
539 views

Why isn't my Q-Learning agent able to play tic-tac-toe?

I tried to build a Q-learning agent which you can play tic tac toe against after training. Unfortunately, the agent performs pretty poorly. He tries to win but does not try to make me 'not winning' ...
6
votes
1answer
2k views

How does Q-learning work in stochastic environments?

The Q function uses the (current and future) states to determine the action that gets the highest reward. However, in a stochastic environment, the current action (at the current state) does not ...
6
votes
2answers
581 views

Can DQN perform better than Double DQN?

I'm training both DQN and double DQN in the same environment, but DQN performs significantly better than double DQN. As I've seen in the double DQN paper, double DQN should perform better than DQN. Am ...
3
votes
2answers
106 views

Why is it not advisable to have a 100 percent exploration rate? [duplicate]

During the learning phase, why don't we have a 100% exploration rate, to allow our agent to fully explore our environment and update the Q values, then during testing we bring in exploitation? Does ...
3
votes
1answer
573 views

Why does Deep Q Network outputs multiple Q values?

I am learning Deep RL following this tutorial: https://medium.freecodecamp.org/an-introduction-to-deep-q-learning-lets-play-doom-54d02d8017d8 I understand everything but one detail: This image shows ...
2
votes
1answer
140 views

What will Q-values look like in self-play tic-tac-toe?

This corresponds to Exercise 1.1 of RLBook, and a discussion followed from here. Considering two reward schemes- Win = +1, Draw = 0, Loss = -1 Win = +1, Draw or Loss = 0 Can we say something about ...
2
votes
1answer
359 views

What exactly is the advantage of double DQN over DQN?

I started looking into the double DQN (DDQN). Apparently, the difference between DDQN and DQN is that in DDQN we use the main value network for action selection and the target network for outputting ...
2
votes
2answers
117 views

What is the target output for updating a Deep Q Network

I'm trying to implement Deep Q-Learning for a pet problem having a continuous state space and discretized action space. The algorithm for table-based Q-Learning updates a single entry of the Q table - ...
1
vote
1answer
70 views

How to build a Neural Network to approximate the Q-function?

I am learning reinforcement learning with Q-learning using online resources, like blog posts, youtube videos, and books. At this point, I have learned the underpinning concepts of reinforcement ...
1
vote
1answer
118 views

Why do my rewards fall using tabular Q-learning as I perform more episodes?

Using the tutorial from: SentDex - Python Programming I added Q Learning to my script that was previously just picking random actions. His script uses the MountainCar Environment so I had to amend it ...
1
vote
1answer
110 views

Deep Q Learning Algorithm for Simple Python Game makes player stuck

I made a simple Python game. A screenshot is below: Basically, a paddle moves left and right catching particles. Some make you lose points while others make you gains points. This is my first Deep Q ...
1
vote
0answers
77 views

OpenAI gym's CartPole problem system does not learn

My OpenAI CartPole-v0 problem's implementation using basic Q-learning does not learn at all. I am a beginner and have implemented my first ever Q-learning from scratch after learning from tutorials. ...