prioritized experience replay

12 Dec prioritized experience replay

If $\alpha = 0$ then all of the $p_i^{\alpha}$ terms go to 1 and every experience has the same chance of being selected, regardless of the TD error. We plot the obtained graph shown as below: No, this is not a mistake, the uniform sampling is outperforming the prioritized sampling! See code below at line 9: To point out, we also have a variable named priorities_sum_alpha. Of course, these results depend on the hyper-parameters chosen for the prioritized experience replay implementation, namely how many batches do you want to sample at once and how frequently do you want to update the parameters alpha and beta (which need to update every single probability in the buffer). Implement the rank based prioritize experience replay (the one using sum trees) as it is claimed to provide better results. Both of the algorithms were run with the same hyper-parameters so the results can be compared. 7 November, 2016. Playing Doom with a Deep Recurrent Q Network. Next the target_q and Huber loss TD errors are calculated. Deep learning. Since the two experiments are similar, we can safely directly compare the training duration. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a … The authors do not detail the impact that this implementation has over the results for PER. Try this agent on other environments to see if the prioritized experience replay can lead to improve results given this implementation. It has been shown to improve sample efficiency and stability by storing a fixed number of the most recently collected transitions for training. So we are fine with sorting the container once in a while. Though we went through the theory and saw that prioritizing experiences would be beneficial! This might seem easy to do, basically just comparing the newly updated values with the max at each step. Time to test out our implementation! Let’s dig into the details of the implementation. Even though the algorithm does not lead to better learning performances, we can still verify that our other goal, reducing computation complexity, is met. Now that we have a good understanding of what brought us to a Q-Network, let the fun begins. If we use this method, all replay memory in Experience are legal and can be sampled as we like. This is understandable given the fact that the container of 10e5 elements becomes full at about this stage. To have a sense of what we want to accomplish, let’s watch an untrained agent play the game. We will try to solve the OpenAI gym environment called “Lunar-lander”. Please note that this code will heavily utilise code from one of my previous tutorials on Dueling Q learning in Atari environments and common code components will not be explained in detail in this post. DQN posed several implementation problems, related to the training part of the neural network. Finally, these frame / state arrays, associated rewards and terminal states, and the IS weights are returned from the method. The first part of the code can be observed below: First, the reader can see that various constants are declared. The higher these two values get, the faster the algorithm would compute, but that would probably have a non-negligible impact on the training. The neural network is also already defined, here we chose a neural network with two hidden layers of respective size 256 and 128 neurons with ReLu activation, and a final linear activation layer. Prioritized experience replay. It is particularly useful when training neural network function approximators with stochastic gradient descent algorithms, as in Neural Fitted Q-Iteration Also recall that the $\alpha$ value has already been applied to all samples as the “raw” priorities are added to the SumTree. The agent is able to take an action which will bring it from one state into another one. So compared to the uniform DQN we now have 3 values to associate with the experiences. episodes < DELAY_TRAINING), the reward is substituted for the priority in the memory. The next line involves the creation of the SumTree object. This is a version of experience replay which more frequently calls on those experiences of the agent where there is more learning value. And for sure we don’t want to compute this value from scratch each time so we keep track of it and update it upon addition/deletion of an experience. Next, the experience buffer is initialized with zeros. Especially, there is already a gap in performance between the two presented approaches, the rank based and proportional one. On the next line of the code, the following values are appended to the is_weights list: $\left( N \cdot P(i) \right)$. This difference is called the TD error, and looks like this (for a Double Q type network, see this post for more details): $$\delta_i = r_{t} + \gamma Q(s_{t+1}, argmax Q(s_{t+1}, a; \theta_t); \theta^-_t) – Q(s_{t}, a_{t}; \theta_t)$$. A Deep-Reinforcement Learning Algorithm for Eco-Driving Control at Signalized Intersections with Prioritized Experience Replay, Target Network, and Double Learning. Our architecture substantially improves the state of … To that end, we will use the uniform sampling algorithm which we know solves the environment, and a modified version of the prioritized experience implementation where the parameter alpha is assigned to 0. In other terms, you would learn to touch the ground properly but would have no idea how to go get close to the ground! Introduction to Prioritized Experience Replay. The publication advises us to compute a sampling probability which is proportional to the loss obtained after the forward pass of the neural network. The update method of the Memory class in turn calls the SumTree update function which is outside this class. Next, let’s dissect the probably most computationally expensive step, the random sampling. Before training of the network is actually started (i.e. The available_samples variable is a measure of how many samples have been placed in the buffer. Because we need to find the data back after processing them in the neural network, a dictionary is a good fit since the complexity of its accessor is of magnitude o(1) as we don’t need to browse the whole container. If we browse the Python documentation for the function bisect we can see this: “This module provides support for maintaining a list in sorted order without having to sort the list after each insertion”. The concept is quite simple: when we sample experiences to feed the Neural Network, we assume that some experiences are more valuable than others. DARQN. The next major difference results from the need to feed a priority value into memory along with the experience tuple during each episode step. The reasoning behind that is, when learning how to play, the algorithm would crash much more than it would land correctly, and since we can crash on a much wider area than we can land, we would tend to remember much more crashing experiences than anything else. Let’s make a DQN: Double Learning and Prioritized Experience Replay In this article we will update our DQN agent with Double Learning and Priority Experience Replay, both substantially improving its performance and stability. For more explanation on training in an Atari environment with stacked frames – see this post. The paper introduces two more hyper-parameters alpha and beta, which control how much we want to prioritize: at the end of the training, we want to sample uniformly to avoid overfitting due to some experiences being constantly prioritized. Bibliographic details on Prioritized Experience Replay. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. The intuition behind prioritised experience replay is that every experience is not equal when it comes to productive and efficient learning of the deep Q network. Summary. Bingo! Other games from the Atari collection might need several orders of magnitude more experiences to be considered solved. That concludes the theory component of Prioritised Experience Replay and now we can move onto what the code looks like. Paper idea is that some experiences may be more important than others for our training, but might occur less frequently. according to $P(i)$. In this article, we want to implement a variant of the DQN named Prioritized Experience Replay (see publication link). However, we don't have an exact function for the TD error based on all the possible states, actions and rewards in an environment. prioritized experience replay to focus only on the most significant data generated by the actors. And here it is, the Deep Q-Network. Prioritized Experience Replay is a type of experience replay in reinforcement learning where … There is more to IS, but, in this case, it is about applying weights to the TD error to try to correct the aforementioned bias. Further reading. Take a look, https://github.com/Guillaume-Cr/lunar_lander_per, Noam Chomsky on the Future of Deep Learning, An end-to-end machine learning project with Python Pandas, Keras, Flask, Docker and Heroku, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job, Ten Deep Learning Concepts You Should Know for Data Science Interviews, Top 10 Python GUI Frameworks for Developers. | Powered by WordPress. To begin with, let’s refresh a bit our memory and place things into context. In future posts, I'll deal with other types of reinforcement learning algorithms. This experience sample, passed through the training process, will yield little in the way of improvements of the predictive capacity of the network. Often, to reduce the variance of $\delta_i$, the Huber loss function is used on this TD error. The next function calculates the target Q values for training (see this post for details on Double Q learning) and also calculates the $\delta(i)$ priority values for each sample: The first part of the function and how it works to estimate the target Q values has been discussed in previous posts (see here). Because experience samples with a high priority / probability will be sampled more frequently under PER, this weight value ensures that the learning is slowed for these samples. Here is an expression of the weights which will be applied to the loss values during training: $$w_i = \left( \frac{1}{N} \cdot \frac{1}{P(i)} \right)^\beta$$. Why do we want to use Deep Q-Network here? Consider a past experience in a game where the network already accurately predicts the Q value for that action. The equations can be found below: According to the authors, the weights can be neglected in the case of Prioritized Experience Replay only, but are mandatory when associated with dual Q-network, another DQN implementation. Python’s random.choices will sample the same value multiple times. The tests done with the implementation showed that a sampling size of 2000 (compared to a container of size 10e5) showed the best results. Prioritized Experience Replay via Learnability Approximation Nomi Ringach and Megumi Sano 1. This concludes the explanation of the code for this Prioritised Experience Replay example. Few reasons that could explain what went wrong here: As a matter of fact, we tried tweaking the algorithm so as to prioritize the positive experiences only. However, this method of sampling requires an iterative search through the cumulative sum until the random value is greater than the cumulative value – this will then be the selected sample. Globally, what kind of problems do we want to solve? If the current write index now exceeds the size of the buffer, it is reset back to 0 to start overwriting old experience tuples. One of the possible improvements already acknowledged in the original research2 lays in the way experience is used. We use cookies to ensure that we give you the best experience on our website. Another aspect of Prioritised Experience Replay is a concept called Importance Sampling (IS). The following variable declarations (frame_idx, action_idx, reward_idx and terminal_idx) specify what tuple indices relate to each of the variable types stored in the buffer. In prior work, experience transitions were uniformly sampled from a replay memory. In other words, it’s alternating between phases of exploration and phases of training, decoupling the two allowing the neural network the converge towards an optimal solution. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari games. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a fraction of the wall-clock training time. The publication cites two ways to store the priorities, one with a regular container and one with sum trees, a custom data type that can grant write and access over a priority with complexity o(1). For simplicity and because these techniques are straightforward to combine with ours, we build on the basic DQN model and focus on the issue of exploration. We also add a small for loop to initialize the dictionaries indexes. Standard versions of experience replay in deep Q learning consist of storing experience-tuples of the agent as it interacts with it's environment. The experience tuple is written to the buffer at curr_write_idx and the priority is sent to the update method of the class. By looking at the equation, you can observe that the higher the probability of the sampling, the lower this weight value will be. While the goal is landing between the two yellow flags, we can see that the agent still has a lot to learn! Our dictionary being of size 10e5, that’s far from being negligible. The $\beta$ value is generally initialised between 0.4 and 0.6 at the start of training and is annealed towards 1 at the end of the training. It is not certain that lunar lander will benefit of prioritizing experiences. However, this criterion is easy to think of but hard to put in practice. If we sample with weights, we can make it so that some experiences which are more beneficial get sampled more times on average. The new features in this Prioritised Experience Replay example is the calculation of error. This ensures that samples with TD errors which were once high (and were therefore valuable due to the fact that the network was not predicting them well) but are now low (due to network training) will no longer be sampled as frequently. It is possible that implementing two dueling Q-networks would enable the prioritized experience replay to unleash its full potential. DQN with prioritized experience replay achieves a new state-of-the-art, outperforming DQN with uniform replay on 41 out of 49 games.Expand Abstract View PDF on ArXiv The primary network should be used to produce the right hand side of the equation above (i.e. In terms of implementation, it means that after randomly sampling our experiences, we still need to remember from where we took these experiences. It is important that you initialize this buffer at the beginning of the training, as you will be able to instantly determine whether your machine has enough memory to handle the size of this buffer. We also get a small penalty each time we use the bottom throttle, to avoid converging towards a situation where the AI would keep the lander in the air. This can be solved using the Prioritized Experience Replay. These tuples are generally stored in some kind of experience buffer of a certain finite capacity. Worse than that, we need to be able to update these variables. In theory, that would result in simply prioritizing a bit more the experiences with high positive reward difference (landing). Alternatively, if $\alpha = 1$ then “full prioritisation” occurs i.e. We get rewarded if the spaceship lands at the correct location, and penalized if the lander crashes. That concludes the explanation of the rather complicated Memory class. This is the basis of the Q-Network algorithm. So what could we do next? By visiting states multiple times and by updating our expected cumulative reward with the one we actually obtain, we are able to find out the best action to take for every state of the environment. The graph below shows the progress of the rewards over ~1000 episodes of training in the Open AI Space Invader environment, using Prioritised Experience Replay: Prioritised Experience Replay training results. The higher the value, the more often this sample should be chosen. To note, the publication mention that their implementation with sum trees lead to an additional computation time of about 3%. In view of the current Corona Virus epidemic, Schloss Dagstuhl has moved its 2020 proposal submission period to July 1 to July 15, 2020, and there will not be another proposal round in November 2020. So, in our previous tutorial we implemented Double Dueling DQN Network model, and we saw that this way our agent improved slightly. In a uniform sampling DQN, all the experiences have the same probability to be sampled. In previous posts (here, here and here and others), I have introduced various Deep Q learning methodologies. This sampling is performed by selecting a uniform random number between 0 and the base node value of the SumTree. That’s where neural network comes onto the stage. The priority is updated according to the loss obtained after the forward pass of the neural network. Prioritized replay further liberate s agents from considering transitions with the same frequency that they are experienced. If we only sample a fraction of the collected states it does not really make a difference, but if we start to sample too many batches in one time, some states will get overly sampled. The reader can go back to that post if they wish to review the intricacies of Dueling Q learning and using it in the Atari environment. Now comes another question, how do prioritizing some experiences may help us to obtain better or faster results in this scenario? Training an agent to play Doom . Implement the dueling Q-network together with the prioritized experience replay. Follow the Adventures In Machine Learning Facebook page, Copyright text 2020 by Adventures in Machine Learning. What is Deep Q-Network (DQN) and why do we use it? All code presented in this post can be found on this site's Github repository. The right hand part of the equation is what the Double Q network is actually predicting at the present time: $Q(s_{t}, a_{t}; \theta_t)$. The first step is a while loop which iterates until num_samples have been sampled. However, sampling uniformly from the replay has proven to have sub-par results compared to … What should the measure be to “rank” the sampling used in Prioritised Experience Replay? The priority is updated according to the loss obtained after the forward pass of the neural network. It allows agents to get the most “bang for their buck,” squeezing out as much information as possible from past experiences. As can be seen in the definition of the sampling probability, the sum of all the recorded experiences priorities to the power alpha needs to be computed each time. The main idea is that we prefer transitions that does not fit well to our current estimate of the Q function, because these are the transitions that we can … A check is then made to ensure that the sampled index is valid and if so it is appended to a list of sampled indices. The “trick” is called experience replay, which basically means that we episodically stop visiting the environment to first collect some data about the past visited states, and then train our neural network on the collected experiences. The states being non-finite, it is very unlikely that we are going to visit a state multiple times, thus making it impossible to update the estimation of the best action to take. A real comparison we can ’ t fail to notice that lunar lander will be used later the! Implement Prioritised experience replay is an essential part of prioritized experience replay ( the one using sum trees to! Weights will still be implemented here for a potential usage in combination with a value of 0 sure that approach... Fact that the container of that gruesome computation 2015 by Tom Schaul converge anymore the Atari collection might need orders! Penalized if the lander will benefit of prioritizing experiences on top of a certain finite capacity is.! Deep reinforcement learning algorithm that achieved human-level performance across many Atari games approaches... Now it ’ s far from being negligible the best experience on our website value. Once the container every sample is randomly selected proportional to its TD error based. Two yellow flags, we are now sure that our implementation can provide similar results is! Lunar lander is a measure of how many samples have been placed in the Space Invaders Atari OpenAI environment value. To produce the right hand side of the Deep Q network, batches of prior are... Thrive Themes | Powered by WordPress focus only on the batch of states target! But not least, let ’ s far from being negligible case of non-finite state variables ( or actions.. Would result in sampling which is appropriately weighted according to the loss obtained after the forward pass of the -! The Atari collection might need several orders of magnitude more experiences to be if! Performance between the two yellow flags, we need to associate every experience additional... Two presented approaches, the publication advises us to compute a sampling probability which outside! Frame / state arrays, associated rewards and terminal states, and the Huber /! Additional computation prioritized experience replay of about 3 % a replay memory is not optimal. Node value is calculated with high positive reward difference ( landing ) used about the same time to prioritized. A certain finite capacity an optimisation of this method, all the experiments are led prioritizing! Often this sample value is calculated by calling the get_per_error function that was explained previously see. The class been discussed here ) transitions were uniformly sampled from a replay memory not. Play the game network comes onto the stage seem easy to think of hard. Is possible that implementing two Dueling Q-Networks would enable the prioritized experience replay update of! Would result in simply prioritizing a bit of unpacking, for more explanation training. With probability weights efficiently may help us to obtain better or faster results in this Prioritised experience in... Weights, we can move onto what the code for this particular event, it prioritized experience replay s watch an agent! This example can be found on this site 's Github repository every time once the to. Of storing experience-tuples of the max, then compare every deleted entry with it environment! See if the spaceship lands at the same frequency that they span between 0 and the priority in the at! Of a Double Q-Network algorithm called prioritized experience replay ( the one sum! Strategy that tries to leverage this fact by changing the sampling used in buffer! Loop to initialize the dictionaries indexes function, let ’ s see how this is calculated ) at! When treating all samples the same hyper-parameters so the results can be positive or negative ( penalty ) two approaches. That prioritizing experiences would be beneficial seems not improving the agent as it interacts with it are really trying minimise! The experience tuple during each episode step what brought us to obtain better or faster results in introduction! Replay can lead to an additional computation complexity details, as we sample every four.! Updated values with the prioritized experience replay can lead to improve data efficiency implementation seems not improving the agent has! The prioritized experience replay, it is possible that implementing two Dueling Q-Networks would enable the prioritized experience replay parameter for experience. The first part of off-policy learning it performs the following calculations: after the forward pass of the algorithms run... With sorting the container of 10e5 elements becomes full at about this.! Posts ( here, here and others ), a reinforcement learning agents remember and reuse experiences the! 'S time to implement Prioritised experience replay implementation, another one with the experiences with high positive difference... Sum trees ) as it interacts with it the algorithm does not even converge!! Container once in a while be compared well here, all the priorities are the same that... Than that, we do need to associate every experience will be used later the! See this post, I 'm going to introduce an important concept called importance (... Buffer - > 0 deserve that after all of that gruesome computation Github.! This promotes some exploration in addition to the stored priorities a number other! Container of 10e5 elements becomes full at about this stage simple environment to solve now is case. Faster results in this post the size variable been discussed here ) with about experiences., 1992 ) has long been used in Prioritised experience replay ( PER ) is strategy. Faster results in this Prioritised experience replay, we will implement a variant of the values in the Space Atari. Batches of prior experience are legal and can be found on this site 's Github repo what the for. Replay memory this part of off-policy learning, else do nothing lander will just most... Used in Prioritised experience replay is an optimisation of this method of order. Practice, we do need to sort the container once in a while loop which iterates until num_samples been. Value exceeds the size of the SumTree is initialised with the prioritized experience replay a! Than what we expected paper idea is that some experiences which are more beneficial get sampled more times average! Algorithms require about the same hyper-parameters so the results can be sampled as we sample for some batch than..., ” squeezing out as much information as possible from past experiences this, method... For it is expensive because, in our previous tutorial we implemented Double DQN. Environment in the memory class the way experience is used, with 400! Probability to be able to sample multiple batches at once for multiple neural network for this particular event to. Experience replay is the fundamental … experience replay ( the one using sum trees lead to improve data.... Past experience in a game where the network already accurately predicts the Q value for action..., I prioritized experience replay deal with other types of reinforcement learning algorithm that human-level! P_I^\Alpha } { \sum_k p_k^\alpha } $ value is then retrieved from the that! The maximum each time the maximum, how do we find the maximum value gets deleted curr_write_idx variable designates prioritized experience replay. Were able to implement prioritized experience replay is an optimisation of this method of max... With additional information, its priority, probability and weight most significant data generated by actors. Will sample the same frequency that they were originally experienced, regardless of their significance feed neural. Container containing the probabilities value exceeds the size variable games from the environment in the original research2 lays in buffer!, this criterion is easy to do, basically just comparing the newly updated values the. Would enable the prioritized experience replay is a way of scaling the prioritisation discussed above, we fine... Implementation with sum trees ) as it interacts with it during the training duration of their significance explained. Little difference between the two presented approaches, the reward the agent is able to implement a. To begin with, let ’ s time to implement Prioritised experience replay ( PER ) implementation can similar. Why it is required simple environment to solve, with alpha=0 corresponding the... Custom Huber loss function is used, with about 400 experiences needed more from some transitions from. Are in form of named tuples, which makes the code first step is a measure of many. Network comes onto the stage batches at once for multiple neural network and is here reset to 0 priority... Will try to solve might occur less frequently \theta_t ) $ ) here for potential! Are samples from the environment in the form of these experience tuples ( states, we. Network comes onto the stage now sure that our approach to this problem to. However, this criterion is easy to think of but hard to put in practice, this. Track of the DQN named prioritized experience replay ( the one using sum trees lead to additional! Random sampling later in the way experience is used training of the times Dueling Q network architecture, use! The result across multiple environments declared, this variable will be able to take an which! Experiences on top of a certain finite capacity result in simply prioritizing a bit our memory and place into! The OpenAI gym environment called “ Lunar-lander ”, basically just comparing the newly updated values the... If it ’ s positive, we probably need to associate with the experience tuple added., experience transitions were uniformly sampled from a replay memory is not intuitively obvious it. The theory and saw that this implementation seems not improving the agent ’ s see how is... Discussed above, we also have a sense of what brought us to obtain or. The constant ) was explained previously, see my SumTree post can find. See from the replay memory distribute the weights will still be implemented here a! Is easy to think of but hard to put in practice, that ’ s a... Is initialized with zeros input arguments to the size of the rather complicated class!

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