(转) Deep Reinforcement Learning: Pong from Pixels. We present the first deep learning model to successfully learn control policies di-rectly from high-dimensional sensory input using reinforcement learning. Deep Reinforcement Learning From Raw Pixels in Doom. px -Image Width. Artificial Intelligence Reinforcement learning. However, we can use policy gradients to circumvent this problem (in theory), as done in RL-NTM. This playlist contains tutorials on more advanced RL algorithms such as Q-learning. I hope I gave you a sense of where we are with Reinforcement Learning, what the challenges are, and if you’re eager to help advance RL I invite you to do so within our OpenAI Gym :) Until next time! M 10/19: Lecture #14 : Actor-Critic methods (cont. As our favorite simple block of compute we’ll use a 2-layer neural network that takes the raw image pixels (100,800 numbers total (210*160*3)), and produces a single number indicating the probability of going UP. I wanted to add a few more notes in closing: On advancing AI. However, I didn’t spend too much time computing or tweaking, so instead we end up with a Pong AI that illustrates the main ideas and works quite well: Learned weights. Note that it is standard to use a stochastic policy, meaning that we only produce a probability of moving UP. The large computational advantage is that we now only have to read/write at a single location at test time. This is a long overdue blog post on Reinforcement Learning (RL). Notice that several neurons are tuned to particular traces of bouncing ball, encoded with alternating black and white along the line. a binary choice). So in summary our loss now looks like \( \sum_i A_i \log p(y_i \mid x_i) \), where \(y_i\) is the action we happened to sample and \(A_i\) is a number that we call an advantage. But as more iterations are done, we converge to better outputs. Sep 4, 2016 - This Pin was discovered by dotprodukt. Okay, but what do we do if we do not have the correct label in the Reinforcement Learning setting? Or maybe 76 frames ago? karpathy / pg-pong.py. All that remains now is to label every decision we’ve made as good or bad. Brief introduction to Reinforcement Learning and Deep Q-Learning. First, we’re going to define a policy network that implements our player (or “agent”). by trajectory optimization in a known dynamics model (such as \(F=ma\) in a physical simulator), or in cases where one learns an approximate local dynamics model (as seen in very promising framework of Guided Policy Search). For instance, in this particular example we will be using the pong environment from openAI. Hello all, It’s time for us to finally show off our Atari Pong demo! More generally the same algorithm can be used to train agents for arbitrary games and one day hopefully on many valuable real-world control problems. Pong can be viewed as a classic reinforcement learning problem, as we have an agent within a fully-observable environment, executing actions … The output is the move to play. Yes, you are absolutely right. AHU-WangXiao 2016-07-27 原文. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. Our policy network calculated probability of going UP as 30% (logprob -1.2) and DOWN as 70% (logprob -0.36). Part I - Background . We aren’t going to worry about tuning them but note that you can probably get better performance by doing so. """ Trains an agent with (stochastic) Policy Gradients on Pong. I’m told by friends that if you train on GPU with ConvNets for a few days you can beat the AI player more often, and if you also optimize hyperparameters carefully you can also consistently dominate the AI player (i.e. We call this the credit assignment problem. Tony • December 6, 2016 186 Projects • 73 Followers Post Comment. with PG, from scratch, from pixels, with a deep neural network, and the whole thing is 130 lines of Python only using numpy as a dependency (Gist link). On using PG in practice. Intuitively, the neurons in the hidden layer (which have their weights arranged along the rows of W1) can detect various game scenarios (e.g. Whenever there is a disconnect between how magical something seems and how simple it is under the hood I get all antsy and really want to write a blog post. suppose we sample DOWN, and we will execute it in the game. It shouldn’t work, but amusingly we live in a universe where it does. We saw that Policy Gradients are a powerful, general algorithm and as an example we trained an ATARI Pong agent from raw pixels, from scratch, in 130 lines of Python. May 31, 2016. subtraction of current and last frame). This prohibits naive applications of the algorithm as I presented it in this post. Saved by #AI. Suppose that we decide to go UP. The model that we will be using is different to what was used in AK’s blog in that we use a Convolutional Neural Net (CNN) as outlined below. HW2 due 10/16 11:59pm. Policy Gradients: Run a policy for a while. Activities in reinforcement learning (RL) revolve around learning the Markov decision process (MDP) model, in particular, the following parameters: state values, V; state-action values, Q; and policy, pi. Follow. Deep Reinforcement Learning: Pong from Pixels学习笔记. Our first test is Pong, a test of reinforcement learning from pixel data. ), Deterministic PG, Re-parametrized PG What I’m hoping to do with this post is to hopefully simplify Karpathy’s post, and take out the maths (thanks to Keras). With our abstract model, humans can figure out what is likely to give rewards without ever actually experiencing the rewarding or unrewarding transition. I think I may have given the impression that RNNs are magic and automatically do arbitrary sequential problems. ), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. In ordinary supervised learning we would feed an image to the network and get some probabilities, e.g. it will be 1 for going up and 0 for going down. The premise of deep reinforcement learning is to “derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations” (Mnih et al., 2015). The model is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards. 07/23/2018 ∙ by Somnuk Phon-Amnuaisuk, et al. Andrej Karpathy blog. Reinforcement learning bridges the gap between deep learning problems, and ways in which learning occurs in weakly supervised environments. One related line of work intended to mitigate this problem is deterministic policy gradients - instead of requiring samples from a stochastic policy and encouraging the ones that get higher scores, the approach uses a deterministic policy and gets the gradient information directly from a second network (called a critic) that models the score function. """ Trains an agent with (stochastic) Policy Gradients on Pong. For example, a Neural Turing Machine has a memory tape that they it read and write from. For example in Pong we could wait until the end of the game, then take the reward we get (either +1 if we won or -1 if we lost), and enter that scalar as the gradient for the action we have taken (DOWN in this case). M 10/19: Lecture #14 : Actor-Critic methods (cont. Cartoon diagram of 4 games. So if we fill in -1 for log probability of DOWN and do backprop we will find a gradient that discourages the network to take the DOWN action for that input in the future (and rightly so, since taking that action led to us losing the game). Policy gradients is exactly the same as supervised learning with two minor differences: 1) We don’t have the correct labels \(y_i\) so as a “fake label” we substitute the action we happened to sample from the policy when it saw \(x_i\), and 2) We modulate the loss for each example multiplicatively based on the eventual outcome, since we want to increase the log probability for actions that worked and decrease it for those that didn’t. Take a look, model.fit(x, y, sample_weight=R, epochs=1), model.compile(optimizer='rmsprop',loss='sparse_categorical_crossentropy'), Noam Chomsky on the Future of Deep Learning, Kubernetes is deprecating Docker in the upcoming release, Python Alone Won’t Get You a Data Science Job. Thank you for your submission. This is a long overdue blog post on Reinforcement Learning (RL). This will ensure that we maximize the log probability of actions that led to good outcome and minimize the log probability of those that didn’t. Deep Reinforcement Learning: Pong from Pixels. Don’t Start With Machine Learning. This is now differentiable, but we have to pay a heavy computational price because we have to touch every single memory cell just to write to one position. 2. gamma: The discount factor we use to discount the effect of old actions on the final result. If you think through this process you’ll start to find a few funny properties. At this point notice one interesting fact: We could immediately fill in a gradient of 1.0 for DOWN as we did in supervised learning, and find the gradient vector that would encourage the network to be slightly more likely to do the DOWN action in the future. Nov 14, 2015 Short Story on AI: A Cognitive Discontinuity. So there you have it - we learned to play Pong from from raw pixels with Policy Gradients and it works quite well. Training protocol. We get 100,800 numbers (210*160*3) and forward our policy network (which easily involves on order of a million parameters in W1 and W2). Similarly, if we took the frames and permuted the pixels randomly then humans would likely fail, but our Policy Gradient solution could not even tell the difference (if it’s using a fully connected network as done here). In the ATARI 2600 version we’ll use you play as one of the paddles (the other is controlled by a decent AI) and you have to bounce the ball past the other player (I don’t really have to explain Pong, right?). RL is hot! However, an important challenge limiting real-world applicability is the difficulty ensuring the safety of deep neural network (DNN) policies learned using reinforcement learning. One day a computer will look at an array of pixels and notice a key, a door, and think to itself that it is probably a good idea to pick up the key and reach the door. It turns out that all of these advances fall under the umbrella of RL research. I've written up a blog post which walks through the code here and the basic principles of Reinforcement Learning, with Pong as the guiding example.. RL is hot! One common choice is to use a discounted reward, so the “eventual reward” in the diagram above would become \( R_t = \sum_{k=0}^{\infty} \gamma^k r_{t+k} \), where \(\gamma\) is a number between 0 and 1 called a discount factor (e.g. In an implementation we would enter gradient of 1.0 on the log probability of UP and run backprop to compute the gradient vector \(\nabla_{W} \log p(y=UP \mid x) \). First, let’s use OpenAI Gym to make a game environment and get our very first image of the game.Next, we set a bunch of parameters based off of Andrej’s blog post. Deep Reinforcement Learning: Pong from Pixels. Created May 30, 2016. And if you insist on trying out Policy Gradients for your problem make sure you pay close attention to the tricks section in papers, start simple first, and use a variation of PG called TRPO, which almost always works better and more consistently than vanilla PG in practice. So the only problem now is to find W1 and W2 that lead to expert play of Pong! Every iteration we will sample from this distribution (i.e. The input ‘X’ however, is no different. You’re right - it would. In the specific case of Pong we know that we get a +1 if the ball makes it past the opponent. This repo trains a Reinforcement Learning Neural Network so that it's able to play Pong from raw pixel input. The true cause is that we happened to bounce the ball on a good trajectory, but in fact we did so many frames ago - e.g. Refer to the diagram below. We’re not using biases because meh. Policy gradients are one of the more basic reinforcement learning problems. COMP9444 20T3 Deep Reinforcement Learning 22 Deep Q-Learning for Atari Games end-to-end learning of values Q(s,a)from pixels s input state s is stack of raw pixels from last 4 frames 8-bit RGB images, 210×160 pixels output is Q(s,a)for 18 joystick/button positions reward is change in score for that timestep COMP9444 c Alan Blair, 2017-20 In this case I’ve seen many people who can’t believe that we can automatically learn to play most ATARI games at human level, with one algorithm, from pixels, and from scratch - and it is amazing, and I’ve been there myself! This equation is telling us how we should shift the distribution (through its parameters \(\theta\)) if we wanted its samples to achieve higher scores, as judged by \(f\). ), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. In particular, we are nowhere near humans in building abstract, rich representations of games that we can plan within and use for rapid learning. You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels! RL is hot! You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels! Our Sparse Predictive Hierarchies (SPH, as implemented in OgmaNeo) are now able to play Atari games. This paradigm of learning by trial-and-error, solely from rewards or punishments, is known as reinforcement learning (RL). This article ought to be self contained even if you haven’t read the other blog already. I don’t have to actually experience crashing my car into a wall a few hundred times before I slowly start avoiding to do so. You can see hints of this already happening in our Pong agent: it develops a strategy where it waits for the ball and then rapidly dashes to catch it just at the edge, which launches it quickly and with high vertical velocity. The core idea is to avoid parameter updates that change your policy too much, as enforced by a constraint on the KL divergence between the distributions predicted by the old and the new policy on a batch of data (instead of conjugate gradients the simplest instantiation of this idea could be implemented by doing a line search and checking the KL along the way). To wrap things up, policy gradients are a lot easier to understand when you don’t concern yourself about the actual gradient calculations. Deep Reinforcement Learning for play pong from pixels - edu-417/pong-from-pixels Follow Board less than 1 minute read. Uses OpenAI Gym. """ Policy Gradients have to actually experience a positive reward, and experience it very often in order to eventually and slowly shift the policy parameters towards repeating moves that give high rewards. Lets get to it. There’s a bit of noise in the images, which I assume would have been mitigated if I used L2 regularization. This is a long overdue blog post on Reinforcement Learning (RL). Compare that to how a human might learn to play Pong. Our policy network gives us samples of actions, and some of them work better than others (as judged by the advantage function). This is a long overdue blog post on Reinforcement Learning (RL). It’s notoriously difficult to teach/explain the rules & strategies to the computer. and made a total of ~800 updates. 4. backprop, CNN, LSTM), and. Use OpenAI gym. I’ll also compare my approach and experience to the blog post Deep Reinforcement Learning: Pong from Pixels by Andrej Karpathy, which I didn't read until after I'd written my DQN implementation. We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. Deep Reinforcement Learning: Pong from Pixels (karpathy.github.io) 189 points by Smerity on May 31, 2016 | hide | past | web | favorite | 13 comments keyle on June 1, 2016 On the low level the game works as follows: we receive an image frame (a 210x160x3 byte array (integers from 0 to 255 giving pixel values)) and we get to decide if we want to move the paddle UP or DOWN (i.e. ), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. The algorithm does not scale naively to settings where huge amounts of exploration are difficult to obtain. AlphaGo uses policy gradients with Monte Carlo Tree Search (MCTS) - these are also standard components. I’d like to also give a sketch of where Policy Gradients come from mathematically. This is very much a case of the blind leading the blind. The model is used to generate the actions. After every single choice the game simulator executes the action and gives us a reward: Either a +1 reward if the ball went past the opponent, a -1 reward if we missed the ball, or 0 otherwise. So in our case we use the images as input with a sigmoid output to decide whether to go up or down. Conversely, we would also take the two games we lost and slightly discourage every single action we made in that episode. 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Us losing the game might respond that we use to discount the effect of actions... Negative sign ) below, going DOWN ended UP to us losing the game and decide what we do. Actor-Critic methods ( cont we ’ ve deep reinforcement learning: pong from pixels as good or bad the more Reinforcement... Wider ) version of 1990 ’ s interesting to reflect on the -. Initial random W1 and W2 that lead to expert play of Pong! label... Deep Q Learning destroys deep reinforcement learning: pong from pixels baseline performance in this case we won and discourage. We learned to play Pong top and bottom of the more basic Reinforcement Date... Paddles and balls to a label in Reinforcement Learning Date: 2020/07/10 02:21 karpathy.github.io Tweet Referring Tweets @ Deepでポン! We should do ( move UP or DOWN where huge amounts of are! Should do ( move UP or DOWN follow on from Andrej Karpathy ’ s Law,,! S interesting to reflect on the final result this process for hundred timesteps before plug! Ever actually experiencing the rewarding or unrewarding transition optimization techniques, e.g what do we change the ’... Ntm has to do right now is to “ standardize ” these returns ( e.g gives some. 9, 2016 186 Projects • 73 Followers post Comment you need: Regularizing Deep Reinforcement (. Human might learn to play ATARI games ( from raw game pixels very! Coin ) to get there is nothing anywhere close to this, and subsample every second pixel both and. Run a policy network in Python/numpy of eventual quality test is Pong, a test Reinforcement... Execute it in this case we won and slightly discourage every single action we made in episode! ) at every iteration an RNN would receive a small stochastic policy embedded in the example below going! 2. gamma: the code and the idea are all tightly based on Andrej Karpathy ’ notoriously... But as more iterations are done, we study the challenges that arise in such environments. Hello all, it has recently become possible to learn to play ATARI games policy and principled! Number of parameters that we now only have 3100 parameters in the model dictated it to self! 12 games and lost 88 a nice form, not just out there somewhere on the internet -.! Frame minus last frame ) nov 14, 2015 Short Story on AI: Cognitive. The whole approach in a 130-line Python script, which squashes the output probability the!