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Dqn keras github

GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. The Actor-Critic algorithm is a model-free, off-policy method where the critic acts as a value-function approximator, and the actor as a policy-function approximator. When training, the critic predicts the TD-Error and guides the learning of both itself and the actor.

In practice, we approximate the TD-Error using the Advantage function. For more stability, we use a shared computational backbone across both networks, as well as an N-step formulation of the discounted rewards. We also incorporate an entropy regularization term "soft" learning to encourage exploration.

While A2C is simple and efficient, running it on Atari Games quickly becomes intractable due to long computation time. In a similar fashion as the A2C algorithm, the implementation of A3C incorporates asynchronous weight updates, allowing for much faster computation. We use multiple agents to perform gradient ascent asynchronously, over multiple threads. We test A3C on the Atari Breakout environment.

The DDPG algorithm is a model-free, off-policy algorithm for continuous action spaces. Similarly to A2C, it is an actor-critic algorithm in which the actor is trained on a deterministic target policy, and the critic predicts Q-Values.

In order to reduce variance and increase stability, we use experience replay and separate target networks. Moreover, as hinted by OpenAIwe encourage exploration through parameter space noise as opposed to traditional action space noise. We estimate target Q-values by leveraging the Bellman equation, and gather experience through an epsilon-greedy policy.

dqn keras github

For more stability, we sample past experiences randomly Experience Replay. For a more accurate estimation of our Q-values, we use a second network to temper the overestimations of the Q-values by the original network.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. To run, python example.

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It runs MsPacman-v0 if no env is specified. Uncomment the env. Currently, it assumes that the observation is an image, i. This is meant to be a very simple implementation, to be used as a starter code. I aimed it to be easy-to-comprehend rather than feature-complete. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Basic DQN implementation. Python Shell. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

Latest commit. Latest commit eee1 Dec 28, Requirements gym keras theano numpy and all their dependencies. Usage To run, python example. Purpose This is meant to be a very simple implementation, to be used as a starter code.

Pull requests welcome! Currently only works for Atari and Atari-like environments where the observation space is a 3D Box. You signed in with another tab or window.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

dqn keras github

If nothing happens, download the GitHub extension for Visual Studio and try again. Implementation of deep Q-network reinforcement learning with deep neural networks and convolutional neural networks to play the game using Keras, Keras-RL and OpenAI Gym. In this project I have implemented an intelligent agent to play the game In particular I have used a reinforcement learning approach Q-learning with different types of deep learning models a deep neural network and 2 types of convolutional neural networks to model the action-value function, i.

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The resulting model goes by the name of deep Q-network DQN and has been made popular by the papers [1][2]. I haven't tested recurrent neural networks given that the game is fully observable hence using past history is probably useless.

Moreover I haven't supervised the NN with well known methods to solve the game like expectimax optimization.

Keras Q-Learning in the OpenAI Gym (12.3)

This because if we use an expectimax optimization approach NNs are not needed at all. I have also tried few data pre-processing techniques log2 normalization and one-hot encoding and I presented the NNs with different data as input. I have obtained the best results by showing to the network input all the possible states of the game-matrix in the next 2 steps I have got this intuition from the Monte Carlo Tree Search method used in the AlphaGo Zero model of Deep Mind.

I have also written few functions to monitor the NN training in real-time similarly to what is done by TensorBoard with TensorFlow given that the model takes a good amount of time to improve via training and having a direct feedback is a good way to cut short the training of unpromising network architectures. Here are some figures showing how the models keeps improving as the training goes on.

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I expect a substantial improvement by training the model for a multiple of this number of training steps. Here we see how we obtain in sequence more games ending withthen and some games ending with This is similar to Fig1, the difference is that here we look at the game-score and not the max tile reached. This curve is slowly increasing meaning that the NN is still learning as the time goes on. This curve is rather similar to the one in Fig3. Before executing the script dqn Here are all the detailed steps:.

The Python script dqn Here are the instructions, respectively:. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Deep Reinforcement Learning to Play with Keras. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Project Description In this project I have implemented an intelligent agent to play the game GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

If nothing happens, download the GitHub extension for Visual Studio and try again. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs.

You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes.

Documentation is available online. You can find more information on each agent in the doc. This is a very simple example and it should converge relatively quickly, so it's a great way to get started! It also visualizes the game during training, so you can watch it learn.

How cool is that? Some sample weights are available on keras-rl-weights. If you have questions or problems, please file an issue or, even better, fix the problem yourself and submit a pull request! To see graphs of your training progress and compare across runs, run pip install wandb and add the WandbLogger callback to your agent's fit call:.

Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Deep Reinforcement Learning for Keras.

Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit.

Latest commit c Nov 11, GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Skip to content. Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

Sign up. Branch: master. Find file Copy path. Raw Blame History. DQN expects a model that has a single output. The problem is that we need to mask the output since we only ever want to update the Q values for a certain action. The way we achieve this is by using a custom Lambda layer that computes the loss. This gives us the necessary flexibility to mask out certain parameters by passing in multiple inputs to the Lambda layer. No need to update the experience memory since we only use the working memory to obtain the state over the most recent observations.

Prepare and validate parameters. In short: it makes the algorithm more stable. We use a dummy target since the actual loss is computed in a Lambda layer that needs more complex input.

However, it is still useful to know the actual target to compute metrics properly. This is used to exponentiate only the diagonal elements, which is done before gathering. This is much easier than only exponentiating the diagonal elements, and, usually, the action space is relatively low. What we compute here is.

dqn keras github

All operations happen over the batch size, which is dimension 0. We don't need targets for mu or L. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Soft vs hard target model updates. Related objects. Validate important input.

We never train the target model, hence we can set the optimizer and loss arbitrarily. Compile model. Create trainable model. The problem is that we need to mask the output since we only.

The way we achieve this is by.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Skip to content.

Permalink Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Branch: master. Find file Copy path. Raw Blame History.

This matters if we store 1M observations. ArgumentParser parser. We use the same model that was described by Mnih et al. You can use every built-in Keras optimizer and even the metrics! We use eps-greedy action selection, which means that a random action is selected with probability eps. We anneal eps from 1. This is done so that the agent initially explores the environment high eps and then gradually sticks to what it knows low eps.

We also set a dedicated eps value that is used during testing. Note that we set it to 0. This ensures that the agent cannot get stuck. If you want, you can experiment with the parameters or use a different policy.

Feel free to give it a try! We capture the interrupt exception so that training can be prematurely aborted. Notice that now you can use the built-in Keras callbacks! You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. In this case, however. Get the environment and extract the number of actions. Next, we build our model. Finally, we configure and compile our agent. You can use every built-in Keras optimizer and. Select a policy. We use eps-greedy action selection, which means that a random action is selected.

This is done so that. The trade-off between exploration and exploitation is difficult and an on-going research topic.Episode: 22 Total reward: Episode: Total reward: Could you tell me if it works robustly or just converges sometimes and what hyperparameters you used?

I tried to execute this code and it never learns anything and I don't know if it's because of the code or if I have some problem with my keras version. Skip to content. Instantly share code, notes, and snippets.

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Copy link Quote reply. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window.

Create the Cart-Pole game environment. Exploration parameters. Network parameters. Memory parameters. Populate the experience memory. Initialize the simulation.

Take one random step to get the pole and cart moving. Make a bunch of random actions and store the experiences. Uncomment the line below to watch the simulation.


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