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All too often, when we deal with data the outcome needed is a strategy or an algorithm itself. To arrive at that strategy we may have historic data or some model on how entities in system respond to various situations. In this write up, I'll go over the method of reinforcement learning. The general idea behind reinforcement learning is to come up with a strategy to maximize some measurable goal. For example, if you are modelling a robot that learns to navigate around obstacles, you want the learning process to come back with a strategy that minimizes collisions (say) with other entities in the environment.

Pattern Recognition and Machine Learning (Information Science and Statistics)

For the sake of simplicity, lets assume the following scenario. A robot is placed (at random) on flat plank of wood which has some sticky glue in the center. To its left there is a hole which damages the robot a bit and to its right is a reward which is its destination, as shown in the figure below

The robot has the following capabilities

- It can sense what state \({S_1,S_2,...,S_7}\) it is in
- It can detect a reward or damage that happens to it while in a particular state
- It can move one space left or right
- It can hop a space on to the right

In order to get reinforcement learning to work, you need to know what the reward values are for each of the states the robot can be in. In this particular example, we will assume the following reward structure for each of the states.

Note, the numbers are fairly arbitrary. In addition to this we need a function or a table, mapping out actions/states pairs leading to new states. Given the robot's movement description above, we can use a table as follows

Zero is being used to designate the scenario when the robot is reset to a random state. With the above two sets of information we are good to start the learning process.

Reinforcement learning works in the following way. You maintain a matrix of values for each state/action pair. This is the reward that can be attained by arriving at a particular state by taking a particular action. But the trick is to also account for what possible future state you can get to, given that you arrive at a given state. For example it may be beneficial to be in a certain state \(X\), but all downstream states from \(X\) may be bad to be in. You want to avoid such states. This way, the algorithm tries to ensure that future favourable and achievable states are taken into account. The algorithm does not immediately update itself based on whatever it learns, it preserves old values and learns gradually. The above methodology can be stated as follows.

If \(Q^{*}(s_t,a_t)\) represents the pay off received by being in state then

$$

Q^{*}(s_t,a_t) = R(s_{t+1},s_t) + \alpha max_{a_t}Q^{*}(s_{t+1},a_{t+1})

$$

To slow down learning a bit, we stick with whatever prior estimate of \(Q^{*}(s_t,a_t)\) we have by a fraction of \(\beta\) as shown below

$$

Q^{*}_{new}(s_t,a_t) = \beta Q^{*}_{prior}(s_t,a_t) + (1 - \beta)Q^{*}(s_t,a_t)

$$

That's it! We now let the system take on various initial states, and let the device play around moving over to different states while we constantly update our \(Q\) matrix. After several iterations, the \(Q\) matrix will end with some values which reflect what strategy to take.

To give a swirl, here is an R code that walks through the entire process for this particular example

A thing to note in the code is how the reward function is encoded. There is a penalty imposed on moving from a higher state to a lower state. This is the simple way to ensure that whatever strategy it comes up with does not involve going backwards from rightward positional gains that have been made. If you try it without the penalty, you will see cases where the strategy does not care going left or right in some states. The above code when run, creates the following output (screen grab below)

The rows represent the states, the columns represent the three possible actions move-left, move-right and hop-right that the robot can take. Notice what it's trying to say:

- State 1, do nothing
- State 2, move right, but don't hop
- State 3, don't move right, hop
- State 4, move right or hop
- State 5, move right (small chance), hop (definitely)
- State 6, move right, move left (small chance)

Reinforcement Learning: State-of-the-Art (Adaptation, Learning, and Optimization)

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Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)

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