# Tag Info

7

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 space to a small number of states. Provided you can sample and update enough times, then a few million states is not a major problem. However, with large state ...

6

tl:dr Read chapter 9 of an Introduction of Reinforcement Learning There is definitely a problem (a curse if you will) when the dimensionality of a task (MDP) grows. For fun, lets extend your problem to a much harder case, continuous variables, and see how we deal with it. Mood: range [-1, 1] // 1 is Happy, 0 is Neutral, -1 is Sad Hunger: range [0, 1] //...

4

The answer is "it depends". Once you have arranged the actions into order, a key trait is whether the action value function has a simple enough shape that sampling from a Gaussian policy function would give consistent expected returns, enough that learning can occur. If the underlying "true" value function has a lot of high frequency ...

3

A stateless RL problem can be reduced to a Multiarmed Bandit (MAB) problem. In such a scenario, taking an action will not change the state of the agent. So, this is the setting of a conventional MAB problem: at each time step, the agent selects an action to either perform an exploration or exploitation move. It then records the reward of the taken action ...

2

Let me rephrase it a little - it's a multidimensional continuous space of actions. So, you assign each action some vector from $R^{n}$. For intuition -- imagine you have a robot arm with four joints. For every joint you could applied a rotation force from [-1, 1] and thus you get a 4-D vector with float numbers for each possible action.

2

Sounds like you have several problems with the way your policy is parametrized. You don't have to use the multivariate normal distribution. It can work, and probably others have done it already (if not with AAC, surely with DDPG, as it'll be easier to derive the policy gradient there). I won't explain how to use the multivariate normal with either case as ...

2

generally the approach is to have a separate head. For example, imagine you have latent vector $z_k$, you would output two values: $h(z_k)$ and $f(z_k)$ where $0 \leq h \leq 1$ and $b_0 \leq f \leq b_1$ where $b_0$ and $b_1$ are your bounds. In thios setup, during inference you would check $h_k$ and if its greater than some threshold (usually .5), youd ...

2

As long as your policy (propensity) is differentiable, everything's is good. Discrete, continuous, other, doesn't matter! :) A common example for continuous spaces is the reparameterization trick, where your policy outputs $\mu, \sigma = \pi(s)$ and the action is $a \sim \mathcal{N}(\mu, \sigma)$.

1

First of all, the support of a normal distribution is the entire real line (or, in general, $\mathbb{R}^n$ for an $n$-dimensional multivariate normal distribution) so your action can be any number in $\mathbb{R}$. What you may be getting confused with is that with probability 0.68 you will obtain an action that is within +/- 1 standard deviation from the ...

1

The question has already been answered by Kirill, but I thought I'll add a good example of a multi-dimensional continuous action space too, namely the one I just encountered in the COBRA paper itself. In all of our experiments we use a 2-dimensional virtual "touch-screen" environment that contains objects with configurable shape, position, and ...

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