Deep Attention Recurrent Q-Network
☆115Nov 7, 2015Updated 10 years ago
Alternatives and similar repositories for DARQN
Users that are interested in DARQN are comparing it to the libraries listed below
Sorting:
- Torch implementation of "Deep Exploration via Bootstrapped DQN"☆42Apr 10, 2016Updated 9 years ago
- Implementation of a simple example of Q learning in Torch.☆51Mar 5, 2017Updated 8 years ago
- Deterministic Policy Gradient using torch7☆43Jun 2, 2016Updated 9 years ago
- Torch7 impementation of: Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images☆43Jan 12, 2016Updated 10 years ago
- A list of deep neural network architectures for reinforcement learning tasks.☆174Aug 9, 2016Updated 9 years ago
- ☆98Aug 25, 2016Updated 9 years ago
- Dynamic Capacity Networks using Tensorflow☆52Feb 15, 2017Updated 9 years ago
- Train an RL agent to play multiple Atari games at once☆69Jun 6, 2016Updated 9 years ago
- Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning☆81Nov 22, 2017Updated 8 years ago
- Using a paper from Google DeepMind I've developed a new version of the DQN using threads exploration instead of memory replay as explain …☆84Mar 4, 2016Updated 9 years ago
- Deep Recurrent Attention Reinforcement Learning in Atari☆82Jul 19, 2018Updated 7 years ago
- Framework and model code for the paper "Language Understanding for Text-based Games using Deep Reinforcement Learning", EMNLP 2015☆127Apr 18, 2016Updated 9 years ago
- Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment Prediction☆20Aug 17, 2017Updated 8 years ago
- Model-Free Episodic Control☆14Jan 12, 2017Updated 9 years ago
- LSTM with associative memory cells (http://arxiv.org/abs/1602.03032)☆109May 1, 2016Updated 9 years ago
- From Pixels to Torques: Policy Learning using Deep Dynamical Convolutional Neural Networks (DDCNN)☆42Nov 3, 2016Updated 9 years ago
- Low-rank Highway Networks☆13Mar 11, 2016Updated 9 years ago
- An implementation of Deep Q-Network using Caffe☆72Oct 29, 2015Updated 10 years ago
- A partial TensorFlow implementation of "Learning Efficient Algorithms with Hierarchical Attentive Memory"☆52Mar 9, 2016Updated 9 years ago
- Asynchronous Methods for Deep Reinforcement Learning☆591Aug 9, 2018Updated 7 years ago
- Hyper-parameter Optimization with DrMAD and Hypero☆23Jun 9, 2016Updated 9 years ago
- RC-NFQ: Regularized Convolutional Neural Fitted Q Iteration. A batch algorithm for deep reinforcement learning. Incorporates dropout regu…☆12Mar 17, 2021Updated 4 years ago
- A Spiking Multi-Layer Perceptron☆33Sep 5, 2017Updated 8 years ago
- Gated Recurrent Unit with Low-rank matrix factorization☆34Mar 11, 2016Updated 9 years ago
- Mixed Incremental Cross-Entropy REINFORCE ICLR 2016☆333Mar 1, 2017Updated 9 years ago
- Multi-Residual Networks☆23Nov 25, 2016Updated 9 years ago
- Deep reinforcement learning package for torch7☆16Sep 17, 2016Updated 9 years ago
- AI Final Project☆65Jan 17, 2016Updated 10 years ago
- Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv.org/abs/1602.01783)☆408Feb 25, 2017Updated 9 years ago
- Persistent advantage learning dueling double DQN for the Arcade Learning Environment☆263Feb 8, 2018Updated 8 years ago
- Top-down Tree LSTM (NAACL 2016) http://aclweb.org/anthology/N/N16/N16-1035.pdf☆83Nov 29, 2016Updated 9 years ago
- Sequence to Sequence Learning Model☆14Jan 9, 2016Updated 10 years ago
- Question Answering via Integer Programming (TableILP)☆28Apr 22, 2016Updated 9 years ago
- Recurrent Neural Network library for Torch7's nn☆943Dec 21, 2017Updated 8 years ago
- Multi-layer Recurrent Neural Networks (LSTM, GRU, RNN) for character-level language models in Torch. Custom modifications to allow better…☆72Sep 27, 2019Updated 6 years ago
- Unsupervised learning of visual concepts from video☆56May 5, 2016Updated 9 years ago
- Tensor Switching Networks☆12Nov 2, 2017Updated 8 years ago
- Value Iteration Networks☆291Apr 21, 2017Updated 8 years ago
- Simple PuddleWorld DQN example using torch7☆29Jun 16, 2016Updated 9 years ago