Alright, now that we got the concept, it’s time to implement on a real case scenario. experience replay we store the agent’s experiences e t5(s t,a t,r t,s t11) at each time-step t in a data set D t5{e 1,…,e t}. Introduction Experience replay is a key technique in reinforcement learning that increases sample efficiency by having the agent repeatedly learn on previous experiences stored in … Now it's time to implement Prioritized Experience Replay (PER) which was introduced in 2015 by Tom Schaul. If it’s positive, we actually got a better reward than what we expected! The alternative to this method of sampling is the SumTree data structure and algorithms. But that’s forgetting that the container is of fixed size, meaning that each step we will also delete an experience to be able to add one more. Second, that this implementation seems not improving the agent’s learning efficiency for this environment. 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. 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 authors do not detail the impact that this implementation has over the results for PER. Now it’s time to implement Prioritized Experience Replay (PER) which was introduced in 2015 by Tom Schaul. Of course, we use the trained agent from the prioritized memory replay implementation, it took more time but it’s still trained well enough! One feasible way of sampling is to create a cumulative sum of all the prioritisation values, and then sample from a uniform distribution of interval (0, max(cumulative_prioritisation)). Questions. Using these priorities, the discrete probability of drawing sample/experience i under Prioritised Experience Replay is: $$P(i) = \frac{p_i^\alpha}{\sum_k p_k^\alpha}$$. If you have been across these posts, you will have observed that a memory buffer is used to recycle older experiences of the agent in the environment and improve learning. The first step is a while loop which iterates until num_samples have been sampled. The problem that we wish to solve now is the case of non-finite state variables (or actions). That’s where neural network comes onto the stage. In order to sample experiences according to the prioritisation values, we need some way of organising our memory buffer so that this sampling is efficient. Also recall that the $\alpha$ value has already been applied to all samples as the “raw” priorities are added to the SumTree. Our architecture substantially improves the state of the art on the Arcade Learning Environment, achieving better final performance in a … Following this, a custom Huber loss function is declared, this will be used later in the code. In this introduction, I'll be using a Dueling Q network architecture, and referring to a previous post on the SumTree algorithm. So we now get 4 variables to associate. We can’t really afford to sort the container every sample, as we sample every four steps. This ensures that the training is not “overwhelmed” by the frequent sampling of these higher priority / probability samples and therefore acts to correct the aforementioned bias. Implement the dueling Q-network together with the prioritized experience replay. Next we initialise the Memory class and declare a number of other ancillary functions (which have already been discussed here). In other terms, you would learn to touch the ground properly but would have no idea how to go get close to the ground! It is possible that implementing two dueling Q-networks would enable the prioritized experience replay to unleash its full potential. $Q(s_{t}, a_{t}; \theta_t)$). One of the possible improvements already acknowledged in the original research2 lays in the way experience is used. This framework is called a Markov Decision Process. Well here, all the priorities are the same so it does happen every time once the container is full. This is calculated by calling the get_per_error function that was explained previously, and this error is passed to the memory append method. All code presented in this post can be found on this site's Github repository. If we sample with weights, we can make it so that some experiences which are more beneficial get sampled more times on average. The next major difference results from the need to feed a priority value into memory along with the experience tuple during each episode step. So we keep track of the max, then compare every deleted entry with it. In terms of implementation, it means that after randomly sampling our experiences, we still need to remember from where we took these experiences. To do that, we will be careful about the types of containers we will use to store our data, as well as how we access and sort out data. In practice, that’s a different story… The algorithm does not even converge anymore! See code below at line 9: To point out, we also have a variable named priorities_sum_alpha. We want to take in priority experience where there is a big difference between our prediction and the TD target, since it … It has been shown to improve sample efficiency and stability by storing a fixed number of the most recently collected transitions for training. For both dictionaries, the values are in form of named tuples, which makes the code clearer. 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 Huber loss function will be used in the implementation below. 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. When treating all samples the same, we are not using the fact that we can learn more from some transitions than from others. This concludes the explanation of the code for this Prioritised Experience Replay example. 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. Now how do we distribute the weights for each experience? This example will be demonstrated in the Space Invaders Atari OpenAI environment. This is a version of experience replay which more frequently calls on those experiences of the agent where there is more learning value. Our AI must navigate towards the fundamental … Implement the rank based prioritize experience replay (the one using sum trees) as it is claimed to provide better results. Importantly, the samples in these training batches are extracted randomly and uniformly across the experience history. Truth be told, prioritizing experiences is a dangerous game to play, it is easy to create bias as well as prioritizing the same experiences over and over leading to overfitting the network for a subset of experiences and failing to learn the game properly. Next, let’s dissect the probably most computationally expensive step, the random sampling. 2.2 Prioritized Experience Replay The main part of prioritized experience replay is the index used to reflect the importance of each transition. Make learning your daily ritual. The next line involves the creation of the SumTree object. Neural networks give us the possibility to predict the best action to take given known states (and their optimal actions) with a non-linear model. For more explanation on training in an Atari environment with stacked frames – see this post. We get rewarded if the spaceship lands at the correct location, and penalized if the lander crashes. Prioritizing too much on them would overfit the neural network for this particular event. To note, the publication mention that their implementation with sum trees lead to an additional computation time of about 3%. This is equivalent to say that we want to keep the experiences which led to an important difference between the expected reward and the reward that we actually got, or in other terms, we want to keep the experiences that made the neural network learn a lot. Of course, the complexity depends on that parameter and we can play with it to find out which value would lead to the best efficiency. The available_samples variable is a measure of how many samples have been placed in the buffer. The curr_write_idx variable designates the current position in the buffer to place new experience tuples. It allows agents to get the most “bang for their buck,” squeezing out as much information as possible from past experiences. First that we were able to implement the prioritized experience replay for deep Q-network with almost no additional computation complexity. Questions. 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. Last but not least, let’s observe a trained agent play the game! The variable N refers to the number of experience tuples already stored in your memory (and will top-out at the size of your memory buffer once it's full). Prioritized Experience Replay (PER) is one of the most important and conceptually straightforward improvements for the vanilla Deep Q-Network (DQN) algorithm. Full code: https://github.com/Guillaume-Cr/lunar_lander_per, Publication: https://arxiv.org/abs/1511.05952, Hands-on real-world examples, research, tutorials, and cutting-edge techniques delivered Monday to Thursday. Once the buffer has been filled for the first time, this variable will be equal to the size variable. For each memory index, the error is passed to the Memory update method. 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. Prioritized Experience Replay Experience replay (Lin, 1992) has long been used in reinforce- ment learning to improve data efficiency. prioritized_replay_beta0 – (float) initial value of beta for prioritized replay buffer The value that is calculated on this line is the TD error, but the TD error passed through a Huber loss function: $$\delta_i = target_q – Q(s_{t}, a_{t}; \theta_t)$$. We need something that can, given two known states close enough to our current state, predict what would be the best action to take in our current state. 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. The Asynchronous Advantage Actor Critic Network. Now, this is very fine when we have a finite number of states, for example when an agent moves through a grid where the state is defined by the case its located at. Playing Doom with a Deep Recurrent Q Network. Prioritized Experience Replay (PER) The idea is that some experiences may be more important than others for our training, but might occur less frequently. Since our algorithm does not provide benefits on this part, it is hard to define optimal parameters, but it should be possible to benchmark a set of parameters and decide what is the best overall compromise. In this article, we will use the OpenAI environment called Lunar Lander to train an agent to play as well as a human! It is natural to select how much an agent can learn from the transition as the criterion, given the current state. Prioritized experience replay. By admin episodes < DELAY_TRAINING), the reward is substituted for the priority in the memory. A common way of setting the priorities of the experience samples is by adding small constant to the TD error term like so: This ensures that, even with samples which have a low $\delta_i$, they still have a small chance of being selected for sampling. 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. Now that we have a good understanding of what brought us to a Q-Network, let the fun begins. A solution to this problem is to use something called importance sampling. Finally, the primary network is trained on the batch of states and target Q values. If the current write index now exceeds the size of the buffer, it is reset back to 0 to start overwriting old experience tuples. Summary. 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). 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