# prioritized experience replay

We can’t really afford to sort the container every sample, as we sample every four steps. What can we conclude in this experiment? Finally, these frame / state arrays, associated rewards and terminal states, and the IS weights are returned from the method. In theory, that would result in simply prioritizing a bit more the experiences with high positive reward difference (landing). In this article, we want to implement a variant of the DQN named Prioritized Experience Replay (see publication link). We are now able to sample experiences with probability weights efficiently. 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 it's time to implement Prioritized Experience Replay (PER) which was introduced in 2015 by Tom Schaul. This would mean o(n) complexity at each step. Next, the states and next_states arrays are initialised – in this case, these arrays will consist of 4 stacked frames of images for each training sample. prioritized_replay_alpha – (float)alpha parameter for prioritized replay buffer. Following the accumulation of the samples, the IS weights are then converted from a list to a numpy array, then each value is raised element-wise to the power of $-\beta$. This method adds the minimum priority factor and then raises the priority to the power of $\alpha$ i.e. 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)). Note that $Q(s_{t}, a_{t}; \theta_t)$ is extracted from the primary network (with weights of $\theta_t$). This weight value will be multiplied by the TD error ($\delta_i$), which has the same effect as reducing the gradient step during training. Experience replay is the fundamental data generating mech- anism in off-policy deep reinforcement learning (Lin,1992). 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. In a uniform sampling DQN, all the experiences have the same probability to be sampled. Time to test out our implementation! Now it’s time to implement Prioritized Experience Replay (PER) which was introduced in 2015 by Tom Schaul. every sample is randomly selected proportional to its TD error (plus the constant). 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}. 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. Looking at the graph, it seems that until 300 episodes, both algorithms require about the same time to process but diverge later. Our dictionary being of size 10e5, that’s far from being negligible. Note that a third argument is passed to the Keras train_on_batch function – the importance sampling weights. When linear interpolation simply consists in “drawing a line between two states”, we need to be able to predict with a higher degree of complexity. Prioritized Experience Replay Experience replay (Lin, 1992) has long been used in reinforce- ment learning to improve data efﬁciency. Now let's look at the results. The difference between these two quantities ($\delta_i$) is the “measure” of how much the network can learn from the given experience sample i. By admin Implement the dueling Q-network together with the prioritized experience replay. 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. The higher the value, the more often this sample should be chosen. This is a version of experience replay which more frequently calls on those experiences of the agent where there is more learning value. In terms of implementation, it means that after randomly sampling our experiences, we still need to remember from where we took these experiences. according to $P(i)$. The concept is quite simple: when we sample experiences to feed the Neural Network, we assume that some experiences are more valuable than others. 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 next method in the Memory class appends a new experience tuple to the buffer and also updates the priority value in the SumTree: Here you can observe that both the experience tuple (state, action, reward, terminal) and the priority of this experience are passed to this method.