# 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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### Why is the reward function $\text{reward} = 1/{(\text{cost}+1)^2}$ better than $\text{reward} =1/(\text{cost}+1)$?

I have implemented a simple Q-learning algorithm to minimize a cost function by setting the reward to the inverse of the cost of the action taken by the agent. The algorithm converges nicely, but ...
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### Is it possible to prove that the target policy is better than the behavioural policy based on learned Q values?

I have retrospective data for a sort of "behaviour policy" which I will use to train a deep q network to learn a target greedy policy. After learning the Q values for this target policy, can we make ...
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 ...
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### Q table not converging for an arbitrary experiment

This is an experiment in order to understand the working of Q table and Q learning. I have the states as states = [0,1,2,3] I have an arbitrary value for each ...
91 views

### Does Q Learning learn from an opponent playing random moves?

I've created a Q Learning algorithm to play Connect Four against an opponent who just chooses a random free column. My Q Agent is currently only winning about 0.49 games on average (30,000 episodes). ...
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### Applying Eligibility Traces to Q-Learning algorithm does not improve results (And might not function well)

I am trying to apply Eligibility Traces to a currently working Q-Learning algorithm. The reference code for the Q-Learning algorithm was taken from this great blog ...
39 views

### Should I just use exploitation after I have trained the Q agent?

When using a trained Q-learning algorithm in an actual game, would I just use exploitation and no longer use exploration? Should I use exploration only during the training phase?
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### Adversarial Q Learning should use the same Q Table?

I'm creating a RF Q-Learning agent for a two player fully-observable board game and wondered, if I was to train the Q Table using adversarial training, should I let both 'players' use, and update, the ...
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### Is Q-Learning suitable for time-dependent spaces?

Many Q-learning techniques have been developed to capture discrete state(observation), actions like a robot in a grid world, and even continuous (state or action) spaces. But I am wondering how we can ...
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### Why is expected sarsa algorithm an off-policy learning? [duplicate]

I am not able to understand why expected sarsa algorithm is an off-policy learning method? How is it that behaviour and target policies can be different? Also please tell under what situation and ...
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### Why are Dueling Q Networks not used more often to approximate Q-values in reinforcement learning algorithms?

I've just learned about Dueling Network Architectures to estimate $Q$-values and am wondering why this architecture is not used more often in deep RL algorithms? DDPG and TD3 estimate the $Q$-function ...
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### What is the target Q-value in DQNs?

I understand that in DQNs, the loss is measured by taking the MSE of outputted Q-values and target Q-values. What does the target Q-values represent? And how is it obtained/calculated by the DQN?
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### Relationship between the reward rate and the sampled reward in a Semi-Markov Decision Process

In the paper: Reinforcement learning methods for continuous-time Markov decision problems, the authors provide the following update rule for the Q-learning algorithm, when applied to Semi-Markov ...
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### Is my understanding of the value function, Q function, policy, reward and return correct?

I'm a beginner in the RL field, and I would like to check that my understanding of certain RL concepts. Value function: How good it is to be in a state S following policy π. ...
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### Why does the policy $\pi$ affect the Q value?

From my understanding, the policy $\pi$ is basically how the agent acts (i.e. the actions it will take in each state). However, I am confused about the Q value and how it is "affected" by a policy. ...
288 views

### Is the Q value the same as the state-action pair value?

Am I right to say that the Q value of a particular state and action is the same as the state-action pair value of that same state and action?
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### How does Monte Carlo Exploring Starts work?

I'm having trouble understanding the 5th step in the flowchart. For the 5th step, the 'update the Q function by taking the average of returns' is confusing. From what I understand, the Q function ...
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### How are n-dimensional vectors state vectors represented in Q-learning?

Using this code: ...
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### Are Q values estimated from a DQN different from a duelling DQN with the same number of layers and filters?

I am confused about the Q values of a duelling deep Q network (DQN). As far as I know, duelling DQNs have 2 outputs Advantage: how good it is to be in a particular state $s$ Value: the advantage of ...
291 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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### Do RNN solves the need for LSTM and/or multiple states in Deep Q-Learning?

Introduction I am trying to setup a Deep Q-Learning agent. I have looked that the papers Playing Atari with Deep Reinforcement Learning as well as Deep Recurrent Q-Learning for Partially Observable ...
82 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 tabular Q-learning, as the Q table would be huge. Is this correct? ...
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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 ...
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 ...
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### How to use convolution neural network in Deep-Q?

I currently have a grid of pixels 20x20. Each pixel can be red green blue or black. So I have one hot-encoded the pixels giving a 20x20x4 array for each screen. For my Deep-Q Network, I have ...
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### Intutitive explanation of why Experience Replay is used in a Deep Q Network?

I understand that Experience Replay is used for data efficiency reasons and to remove correlations in sequences of data. How exactly do these sequences of correlated data affect the performance of the ...
56 views

### How should I define the state space for this life science problem?

I would like to ask for a piece of advice with regard to Q-learning. I am studying RL and would like to do a basic project applied to life science and calculate the reward. I have been trying to get ...
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### How is the expected value in the loss function of DQN approximated?

In Deep Q Learning the parametrized Q-functions $Q_i$ are optimised by performing gradient descent on the series of loss functions $L_i(\theta_i)= E_{(s,a)\sim p}[(y_i-Q(s,a;\theta_i))^2]$ , where ...
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### Is there an advantage in decaying $\epsilon$ during Q-Learning?

If the agent is following an $\epsilon$-greedy policy derived from Q, is there any advantage to decaying $\epsilon$ even though $\epsilon$ decay is not required for convergence ?
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### How would one develop an action space for a game that is proprietary?

I'm currently trying to develop an RL that will teach itself to play the popular fighting game "Tekken 7". I initially had the idea of teaching it to play generally- against actual opponents with ...
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### Can Google's patented ML algorithms be used commercially?

I just find that Google patents some of the widely used machine learning algorithms. For example: System and method for addressing overfitting in a neural network (Dropout?) Processing images using ...