I want to add noise to the reward distribution I have.
In what format should the reward distribution be represented for VW to understand and
what methods are available in VW to induce the noise?
For example, you can consider the reward distribution given here
Reward function are essential for a good reinforcement learning algorithm. In your simulation reward function is contributing to reward distribution so one way to add noise to the reward distribution could be (considering the tutorial) to modify your cost function to give random results. So in this way you can add noise to distribution.
Related
In deep reinforcement learning, is there any way to decay learning rate wrt to cumulative reward. I mean, decay learning rate when the agent is able to learn and maximize the reward?
It is common to modify learning rates with number of steps, so it would certainly be possible to modify learning rates as a function of cumulative reward.
One risk would be that you do not know what reward you are seeking at the beginning of training, so reducing the learning rate too early is a common problem. If you target a reward of 80, with the learning rate declining sharply as you attain that value, you will never know if your algorithm could have attained 90, as learning will stop at 80.
Another problem is setting the target too high. If you set the target for 100, meaning that the learning rate does not reduce as you reach 85, the instability may mean that the algorithm cannot converge well enough to reach 90.
So in general, I think people try a variety of learning schedules, and if possible sometimes let the algorithms run for plenty of time to see if they converge.
I have trained an RL agent in an environment similar to the Puckworld. Theres no puck though! The agent is in continuous space and wants to reach a fixed target. Each episode the agent is born at a random location and there is an added noise to each action to make learning less trivial.
The reward is given every step as a scaled version of the distance to the target.
I want to plot the convergence of the neural network. The same problem in discrete space and using Q learning, I would plot the sum of all elements in Q matrix vs episode number. This gave me a good understanding of the performance of the network. How can i do the same for a neural network?
Plotting the reward collected in an episode vs episode number is not optimal here.
I use PyTorch. Any help is appreciated
I'm wondering how to plot reward curves in reinforcement learning.
Especially, my simulated environment has significant randomness.
So there are so many zig-zag patterns in raw data of reward even though the output policy is converged.
Is there any way to plot in this case?
I am afraid I don't get your problem. Why not just plotting the reward you receive at each episode? If the policy converges, after a while you should see an increase in the reward, even though there might be those zig zags st start.
I am learning about the approach employed in Reinforcement Learning for robotics and I came across the concept of Evolutionary Strategies. But I couldn't understand how RL and ES are different. Can anyone please explain?
To my understanding, I know of two main ones.
1) Reinforcement learning uses the concept of one agent, and the agent learns by interacting with the environment in different ways. In evolutionary algorithms, they usually start with many "agents" and only the "strong ones survive" (the agents with characteristics that yield the lowest loss).
2) Reinforcement learning agent(s) learns both positive and negative actions, but evolutionary algorithms only learns the optimal, and the negative or suboptimal solution information are discarded and lost.
Example
You want to build an algorithm to regulate the temperature in the room.
The room is 15 °C, and you want it to be 23 °C.
Using Reinforcement learning, the agent will try a bunch of different actions to increase and decrease the temperature. Eventually, it learns that increasing the temperature yields a good reward. But it also learns that reducing the temperature will yield a bad reward.
For evolutionary algorithms, it initiates with a bunch of random agents that all have a preprogrammed set of actions it is going to do. Then the agents that has the "increase temperature" action survives, and moves onto the next generation. Eventually, only agents that increase the temperature survive and are deemed the best solution. However, the algorithm does not know what happens if you decrease the temperature.
TL;DR: RL is usually one agent, trying different actions, and learning and remembering all info (positive or negative). EM uses many agents that guess many actions, only the agents that have the optimal actions survive. Basically a brute force way to solve a problem.
I think the biggest difference between Evolutionary Strategies and Reinforcement Learning is that ES is a global optimization technique while RL is a local optimization technique. So RL can converge to a local optima converging faster while ES converges slower to a global minima.
Evolution Strategies optimization happens on a population level. An evolution strategy algorithm in an iterative fashion (i) samples a batch of candidate solutions from the search space (ii) evaluates them and (iii) discards the ones with low fitness values. The sampling for a new iteration (or generation) happens around the mean of the best scoring candidate solutions from the previous iteration. Doing so enables evolution strategies to direct the search towards a promising location in the search space.
Reinforcement learning requires the problem to be formulated as a Markov Decision Process (MDP). An RL agent optimizes its behavior (or policy) by maximizing a cumulative reward signal received on a transition from one state to another. Since the problem is abstracted as an MDP learning can happen on a step or episode level. Learning per step (or N steps) is done via temporal-Difference learning (TD) and per episode is done via Monte Carlo methods. So far I am talking about learning via action-value functions (learning the values of actions). Another way of learning is by optimizing the parameters of a neural network representing the policy of the agent directly via gradient ascent. This approach is introduced in the REINFORCE algorithm and the general approach known as policy-based RL.
For a comprehensive comparison check out this paper https://arxiv.org/pdf/2110.01411.pdf
My goal is to predict customer churn. I want to use reinforcement learning to train a recurrent neural network which predicts a target response for its input.
I understand that the state is represented by the input to the network at each time, but I don't understand how the action is represented. Is it the values of weights which the neural network should decide to choose by some formulas?
Also, how should we create a reward or punishment to teach the neural network its weights as we don't know the target response for each input neurons?
The aim of reinforcement learning is typically to maximize long term reward for an agent playing a game of sorts (a Markov Decision Process). In typical reinforcement learning usage, neural networks are used to approximate the Q-function. So, the network's input is the state and action (or a feature representations thereof), and the output is the value of taking that action in that state. Reinforcement learning algorithms like Q-learning provide the details on how to choose actions at a given time step, and also dictate how updates to the value function should be done.
It isn't clear how your specific goal of building a customer churn model might be formulated as a Markov Decision Problem. You could define your states to be statistics about customers' interactions with the company website, but it isn't clear what the actions might be, because it isn't clear what the agent is and what it can do. This is also why you are finding it difficult to define a reward function. The reward function should tell the agent if it's doing a good job. So, if we're imagining an MDP where the agent is trying to minimize customer churn, we might provide a negative reward proportional to the number of customers that turn over.
I don't think you want to learn a Q-function. I think it's more likely that you are interested simply in supervised learning, where you have some sample data and you want to learn a function that will tell you how much churn there will be. For this, you should be looking towards gradient descent methods and forward/backward propagation for training your neural network.