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My best guess that it's been done to reduce the computation time, otherwise we would have to find out the q value for each action and then select the best one.
It has no real impact on computation time, other than a slight increase (due to extra memory used by two networks). You could cache results of the target network I suppose, but it probably would not ...
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Here is a table that attempts to systematically show the differences between tabular Q-learning (TQL), deep Q-learning (DQL), and deep Q-network (DQN).
Tabular Q-learning (TQL)
Deep Q-learning (DQL)
Deep Q-network (DQN)
Is it an RL algorithm?
Yes
Yes
No (unless you use DQN to refer to DQL, which is done often!)
Does it use neural networks?
No. It uses a ...
8
The speaker argued that a dream is a random addition of memories, just as experience replay.
The speaker is taking some liberties due to a general lack of scientific understanding of what dreams are. We don't even have strong consensus on why sleep is a necessary feature of animals, let alone what part dreaming plays in it. However, there are some widely-...
8
Gradient descent and back-propagation
In deep learning, gradient descent (GD) and back-propagation (BP) are used to update the weights of the neural network.
In reinforcement learning, one could map (state, action)-pairs to Q-values with a neural network. Then GD and BP can be used to update the weights of this neural network.
How to design the neural ...
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
DQN and AlphaZero do not share much in terms of implementation.
However, they are based on the same Reinforcement Learning (RL) theoretical framework. If you understand terms like MDP, reward, return, value, policy, then these are interchangeable between DQN and AlphaZero. When it comes to implementation, and what each part of the system is doing, then this ...
6
To model chess as a Markov decision problem (MDP) you can refer to the AlphaZero paper (Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm). The exact details can be found starting from the bottom of page 13.
Briefly, an action is described by picking a piece and then picking a move with it. The size of the board is 8 by ...
6
The authors of that paper hypothesized that rank-based prioritization would be more robust to outliers. They suggested that rank-based sampling would be preferred for this reason. However, as they noted later, the fact that DQN clips rewards anyways weakens this argument.
If you're going to use someone else's ready-made code for your prioritized experience ...
5
1) Is there any way to set the initial Q-values for the actions?
You can generally do this, but you cannot specify specific weights for specific actions in specific states. Not through the network weights directly, at least. That would defeat the purpose of using backpropagation to optimize the weights and find the optimal parameters and Q-values.
2) Is ...
5
Let $Q^*(s, a)$ denote the "true" $Q$-value for a state-action pair $(s, a)$, i.e. the values that we're hoping to learn to approximate using a neural network that outputs $Q(s, a)$ values.
The problem you describe is basically that you have situations where $Q^*(s, a_1) = Q^*(s, a_2) + \epsilon$ for some very small value $\epsilon$, where $a_1 \neq a_2$.
...
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More precisely: is DQNN applicable only when we have high translational invariance in our input(s)?
No, DQN (that is the commonly-used abbreviation by the way, there also is a dqn tag which you may wish to use) is not restricted to images or other kinds of inputs with those properties, it can be used with pretty much any kinds of inputs.
The DQN algorithm ...
5
I will try to give a broad answer, if it's not helpful I'll remove it.
When we talk about sampling we are actually talking about the number of interaction required to an agent to learn a good model of the environment.
In general I would say that there are two issues related to sample efficiency:
1 the size of the 'action'+'environment states' space 2 the ...
5
This answer will point the reader to potentially useful resources, but I can't ensure that the courses are good (because I have never followed them).
Free
Reinforcement Learning in the Open AI Gym (a small course that you can find in the YouTube channel suggested in the other answer) by Phil Tabor
The free course Advanced Deep Learning & Reinforcement ...
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In Q-learning (and in general value based reinforcement learning) we are typically interested in learning a Q-function, $Q(s, a)$. This is defined as
$$Q(s, a) = \mathbb{E}_\pi\left[ G_t | S_t = s, A_t = a \right]\;.$$
For tabular Q-learning, where you have a finite state and action space you can maintain a table lookup that maintains your current estimate ...
4
Usually when people write about having a high-dimensional state space, they are referring to the state space actually used by the algorithm.
Suppose my state is a high dimensional vector of $N$ length where $N$ is a huge number. Let's say I solve this task using $Q$-learning and I fix my state space to $10$ vectors each of $N$ dimensions. $Q$-learning can ...
4
After some research and reading this post, I see where my problem was: I was introducing a full consecutive batch of experiences, selected randomly, yes, but the experiences in the batch were consecutives.
After redoing my experience selection method, my DQN is actually working and has reached about +200 points after 400000 experiences (about 500 episodes; ...
4
I think this was just a "clever" design choice. You can actually design a neural network (NN), to represent your Q function, which receives as input the state and an action and outputs the corresponding Q value. However, to obtain $\max_aQ(s', a)$ (which is a term of the update rule of the Q-learning algorithm) you would need a "forward pass" of this network ...
4
The simplest thing to do when you make you first implementation of the agent, is to automate decisions like this, in order to keep representations and decisions simple.
However, if you want to explore tactics surrounding declaration, then I think the following applies:
There should be an initial round of actions where the agent may get to decide whether or ...
4
In a two player zero-sum game (if I win, you lose and vice-versa), then you can have a simple and efficient solution learning from self-play.
How should an opponent be implemented to get good and fast improvements?
You don't need to think in terms of agent vs opponent, instead think in terms of coding both the players' goals into a single Q function. ...
4
In some cases we may wish to have a discount factor $\gamma_t$ which depends on time $t$ (or depends on state $s_t$ and/or action $a_t$, leading to an indirect dependence on time $t$). Indeed we do not usually do this, but it does happen sometimes.
I guess that, from a theoretical point of view, it was very easy of the authors to make their algorithm more ...
4
We can't say for sure which approach would work best in the general case. If you have domain knowledge, you can make a better guess. You'll basically want to answer the question: which information is important for learning an optimal policy?
In my environment, I have, for each pixel, 5 possible channels, which are represented in black, white, blue, red, and ...
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This is mostly because humans already have information when they start learning the game (priors) that makes them learn it more quickly. We already know to jump on monsters or avoid them or to get gold looking object.
When you remove these priors you can see a human is worse at learning these games. (link)
Some experiments they tried in the study to ...
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For the programming part I suggest this YouTube channel by Phil Tabor (he also has a website: neuralnet.ai. I found his videos really useful while I was attending reinforcement learning classes at the uni. He covers basic algorithms like value iteration and policy iteration and also more advanced like deep q learning, covering all main python libraries (...
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Dueling-DQN has different network architecture comparing to vanilla DQN, so I don't think your version will work as well as the Dueling architecture.
From Wang et al., 2016, Dueling Network Architectures for Deep Reinforcement Learning
On the other hand, since we only have the target Q-value, separating the Q-value into state value and advantage result ...
4
When training a Deep Q network with experienced replay, you accumulate what is known as training experiences $e_t = (s_t, a_t, r_t, s_{t+1})$. You then sample a batch of such experiences and for each sample you do the following.
Feed $s_t$ into the network to get $Q(s,a;\theta)$.
Feed $s_{t+1}$ into the network to get $Q(s’,a’,\theta)$.
Choose $max_aQ(s’,a’,...
4
The update form $\theta^{\prime} \leftarrow \tau \theta+(1-\tau) \theta^{\prime}$ (where $\theta'$ and $\theta$ represent the weights of the target network and the current network, respectively) does exist and is correct.
It is called soft update and it has been used in the Deep Deterministic Policy Gradient (DDPG) paper, which uses the concept of a target ...
4
MDPs are strict generalisations of contextual bandits, adding time steps and state transitions, plus the concept of return as a measure of agent performance.
Therefore, methods used in RL to solve MDPs will work to solve contextual bandits. You can either treat a contextual bandit as a series of 1-step episodes (with start state chosen randomly), or as a ...
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DQN on the other hand, explores using epsilon greedy exploration. Either selecting the best action or a random action.
This is a very common choice, because it is simple to implement and quite robust. However, it is not a requirement of DQN. You can use other action choice mechanisms, provided all choices are covered with a non-zero probability of being ...
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Q-learning is said to be "model-free". Given the two examples above, is it because neither the lake's topology nor that of the mountain are changed by the actions taken?
No. That's not why Q-learning is model-free. Q-learning assumes that the underlying environment (FrozenLake or MountainCar, for example) can be modelled as a Markov decision ...
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I don't think that (at least from a practical standpoint), there is much difference between continuous action space and discrete action space with >2k discrete actions. Quoting the "Continuous control with Deep RL" paper - which I'd recommend as a starting point for your investigation:
An obvious approach to adapting deep reinforcement learning ...
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