Package: pytorch-policy
Deep Reinforcement Learning in PytorchThis repository contains all standard model-free and model-based(coming) RL algorithms in Pytorch. (May also contain some research ideas I am working on currently)What is it?pytorch-rl implements some state-of-the art deep reinforcement learning algorithms in Pytorch, especially those concerned with continuous action spaces. You can train your algorithm efficiently either on CPU or GPU. Furthermore, pytorch-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 pytorch-rl according to your own needs.
TL:DR : pytorch-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms.DependenciesPytorchGym (OpenAI)mujoco-py (For the physics simulation and the robotics env in gym)Pybullet (Coming Soon)MPI (Only supported with mpi backend Pytorch installation)Tensorboardx (https://github.com/lanpa/tensorboardX)RL algorithmsDQN (with Double Q learning)DDPGDDPG with HER (For the OpenAI Fetch Environments)Heirarchical Reinforcement LearningPrioritized Experience Replay + DDPGDDPG with Prioritized Hindsight experience replay (Research)Neural Map with A3C (Coming Soon)Rainbow DQN (Coming Soon)PPOHER with self attention for goal substitution (Research)A3C (Coming Soon)ACER (Coming Soon)DARLATDMWorld ModelsSoft Actor-CriticEnvironmentsBreakoutPong (coming soon)Hand Manipulation Robotic TaskFetch-Reach Robotic TaskHand-Reach Robotic TaskBlock Manipulation Robotic TaskMontezuma's Revenge (Current Research)PitfallGravitarCarRacingOpenSim Prosthetics Nips Challenge (https://www.crowdai.org/challenges/nips-2018-ai-for-prosthetics-challenge)Environment Modelling (For exploration and domain adaptation)Multiple GAN training tricks have been used because of the instability in training the generators and discriminators.
Please refer tohttps://github.com/soumith/ganhacksfor more information.Even after using the tricks, it was really hard to train a GAN to convergence.
However, after using Spectral Normalization (https://arxiv.org/abs/1802.05957) the infogan was trained to convergence.For image to image translation tasks with GANs and for VAEs in general, training with Skip Connection really helps the training.beta-VAEInfoGANCVAE-GANFlow based generative models (Research)SAGANSequential Attend, Infer, RepeatCuriosity driven explorationParameter Space Noise for ExplorationNoisy NetworkReferencesPlaying Atari with Deep Reinforcement Learning, Mnih et al., 2013Human-level control through deep reinforcement learning, Mnih et al., 2015Deep Reinforcement Learning with Double Q-learning, van Hasselt et al., 2015Continuous control with deep reinforcement learning, Lillicrap et al., 2015CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training, Bao et al., 2017beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework, Higgins et al., 2017Hindsight Experience Replay, Andrychowicz et al., 2017InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets, Chen et al., 2016World Models, Ha et al., 2018Spectral Normalization for Generative Adversarial Networks, Miyato et al., 2018Self-Attention Generative Adversarial Networks, Zhang et al., 2018Curiosity-driven Exploration by Self-supervised Prediction, Pathak et al., 2017Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, Haarnoja et al., 2018Parameter Space Noise for Exploration, Plappert et al., 2018Noisy Network for Exploration, Fortunato et al., 2018Proximal Policy Optimization Algorithms, Schulman et al., 2017