BY571 / FQF-and-ExtensionsLinks
PyTorch implementation of the state-of-the-art distributional reinforcement learning algorithm Fully Parameterized Quantile Function (FQF) and Extensions: N-step Bootstrapping, PER, Noisy Layer, Dueling Networks, and parallelization.
☆33Updated 5 years ago
Alternatives and similar repositories for FQF-and-Extensions
Users that are interested in FQF-and-Extensions are comparing it to the libraries listed below
Sorting:
- PyTorch implementation of the Munchausen Reinforcement Learning Algorithms M-DQN and M-IQN☆45Updated 5 years ago
- PyTorch implementation of FQF, IQN and QR-DQN.☆186Updated last year
- PyTorch Implementation of Implicit Quantile Networks (IQN) for Distributional Reinforcement Learning with additional extensions like PER,…☆88Updated 2 years ago
- Soft Actor-Critic with advanced features☆51Updated last week
- Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)☆147Updated 6 years ago
- Implementation of Deep Reinforcement Learning from Self-Play in Imperfect-Information Games (Heinrich and Silver, 2016)☆46Updated 6 years ago
- Pytorch implementation of distributed deep reinforcement learning☆76Updated 3 years ago
- A framework for easy prototyping of distributed reinforcement learning algorithms☆96Updated 4 years ago
- Combining Evolutionary Algorithms and deep RL in various ways☆106Updated 4 years ago
- Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch with some e…☆53Updated 4 years ago
- PyTorch Implementation of Ape-X (Distributed prioritized experience replay) architecture with DQN learner☆27Updated 5 years ago
- A Modular Library for Off-Policy Reinforcement Learning with a focus on SafeRL and distributed computing☆135Updated 2 months ago
- Modified versions of the SAC algorithm from spinningup for discrete action spaces and image observations.☆97Updated 5 years ago
- 🐳 Implementation of various Distributional Reinforcement Learning Algorithms using TensorFlow2.☆69Updated 4 years ago
- PyTorch - Implicit Quantile Networks - Quantile Regression - C51☆22Updated 6 years ago
- MetaGenRL, a novel meta reinforcement learning algorithm. Unlike prior work, MetaGenRL can generalize to new environments that are entire…☆67Updated 5 years ago
- FQF(Fully parameterized Quantile Function for distributional reinforcement learning) is a general reinforcement learning framework for At…☆46Updated 5 years ago
- NeurIPS 2019: DQN(λ) = Deep Q-Network + λ-returns.☆24Updated last year
- Implementation of Bootstrap DQN and Randomized Prior Functions on ALE☆55Updated 7 months ago
- ☆30Updated 2 years ago
- Evolution-based Soft Actor-Critic (ESAC)☆42Updated last year
- Random network distillation on Montezuma's Revenge and Super Mario Bros.☆52Updated 5 months ago
- Official repo for our AAAI'21 paper, https://arxiv.org/abs/2007.12354☆27Updated 4 years ago
- This repository contains the implementation for the paper - Exploration via Hierarchical Meta Reinforcement Learning.☆62Updated 6 years ago
- DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow,…☆121Updated 4 years ago
- PyTorch implementation of QR-DQN: Distributional Reinforcement Learning with Quantile Regression☆29Updated 5 years ago
- A PyTorch implementation of SEED, originally created by Google Research for TensorFlow 2.☆14Updated 4 years ago
- Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.☆214Updated 2 years ago
- Python interface for accessing the near real-world offline reinforcement learning (NeoRL) benchmark datasets☆127Updated 11 months ago
- Qiita投稿用に作成したAgent57(強化学習)の実装コードです。☆45Updated 2 years ago