# Tag Info

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

Q-learning for continuous state spaces Yes, this is possible, provided you use some mechanism of approximation. One approach is to discretise the state space, and that doesn't have to reduce the ...
• 31.5k
Accepted

### Can neural networks have continuous inputs and outputs, or do they have to be discrete?

Neural networks normally work in continuous spaces. A typical neural network function could be written as $f(\mathbf{x}, \mathbf{\theta}): \mathbb{R}^N \rightarrow \mathbb{R}^M$. That is, a function ...
• 31.5k

### Model-based learning in continuous state and action spaces

You can use function approximation like neural networks to learn the whole environment, i.e. both the transition function, $p(s'\mid s, a)$, and the reward model, $r(s,a,s')$: $$p(s',r\mid s,a)$$ In ...
• 2,748
1 vote

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

Q-Learning for continuous state space Reinforcement learning algorithms (e.g Q-Learning) can be applied to both discrete and continuous spaces. If you understand how it works in discrete mode, then ...
• 579
1 vote
Accepted

### Model-based RL algorithms for continuous state space and finite action space

In optimal control field to minimize certain well-defined costs especially in process industries, continuous state space model-based planning methods such as model predictive control (MPC) is a common ...
• 1,178
1 vote
Accepted

### What would be the Bellman optimality equation for $q_∗(s, a)$ for an MDP with continuous states and actions?

I think your equations are alright. Anyway, this is just a question of mathematical notation. In measure theory, a discrete random variable $X$ is said to have a counting measure over it's support \$\...
1 vote
Accepted

### Reinforcement learning algorithms for large problems that are not based on a neural network

There are many state-of-the-art reinforcement learning algorithms for large problems with multidimensional continuous state spaces and actions. All of them rely on some sort of function approximator. ...
• 2,860

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