- DeepRL-Tutorials: Pytorch implementation of DQNs, Multi-step Returns, Double DQN, Dueling DQN, Prioritized Replay, Noisy Networks for Exploration, Categorical DQN (C51), Rainbow, and Distributional DQN with Quantile Regression https://github.com/qfettes/DeepRL-Tutorials 4 comments reinforcementlearning
Linked pages
- Sutton & Barto Book: Reinforcement Learning: An Introduction http://incompleteideas.net/book/the-book-2nd.html 44 comments
- https://arxiv.org/abs/1710.10044 9 comments
- https://arxiv.org/abs/1602.01783 7 comments
- OpenAI Baselines: ACKTR & A2C https://blog.openai.com/baselines-acktr-a2c/ 6 comments
- https://arxiv.org/abs/1707.06347 3 comments
- GitHub - openai/baselines: OpenAI Baselines: high-quality implementations of reinforcement learning algorithms https://github.com/openai/baselines 3 comments
- [1506.02438] High-Dimensional Continuous Control Using Generalized Advantage Estimation https://arxiv.org/abs/1506.02438 3 comments
- [1507.06527] Deep Recurrent Q-Learning for Partially Observable MDPs https://arxiv.org/abs/1507.06527 3 comments
- [1706.10295] Noisy Networks for Exploration https://arxiv.org/abs/1706.10295 0 comments
- [1710.02298] Rainbow: Combining Improvements in Deep Reinforcement Learning https://arxiv.org/abs/1710.02298 0 comments
- [1511.06581] Dueling Network Architectures for Deep Reinforcement Learning http://arxiv.org/abs/1511.06581 0 comments
- [1707.06887] A Distributional Perspective on Reinforcement Learning https://arxiv.org/abs/1707.06887 0 comments
- [1509.06461] Deep Reinforcement Learning with Double Q-learning http://arxiv.org/abs/1509.06461 0 comments
- GitHub - ikostrikov/pytorch-a2c-ppo-acktr-gail: PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL). https://github.com/ikostrikov/pytorch-a2c-ppo-acktr 0 comments