This is the official implementation for the paper "Plan2Align: Predictive Planning Based Test-Time Preference Alignment in Paragraph-Level Machine Translation"

A SpaceX Rocket Lander environment for OpenAI gym using Box2D

Asilomar 2020 code for Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile Networks

[Reimplementation Ross et al 2011] An implementation of DAGGER using ConvNets for driving from pixels.

Pytorch implementation of Neural Processes for functions and images 🎆

An ns-3 module for simulations of power line communication networks

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

User facing library for accessing the Ushiriki Policy Engine webservice API

Modularized Implementation of Deep RL Algorithms in PyTorch

ns3-gym - The Playground for Reinforcement Learning in Networking Research

Implementation of selected reinforcement learning algorithms in Tensorflow. A3C, DDPG, REINFORCE, DQN, etc.

Implementations of Reinforcement Learning Models in Tensorflow

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

Implementation of the Deep Deterministic Policy Gradient (DDPG) using PyTorch

A simple framework for experimenting with Reinforcement Learning in Python.

Pytorch Implementation of DQN / DDQN / Prioritized replay/ noisy networks/ distributional values/ Rainbow/ hierarchical RL

Implementation of 'A Distributional Perspective on Reinforcement Learning' and 'Distributional Reinforcement Learning with Quantile Regression' based on OpenAi DQN baselines.

Bayesian optimization for Python

A fork of OpenAI Baselines, implementations of reinforcement learning algorithms

Author's PyTorch implementation of TD3 for OpenAI gym tasks

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

Collection of Deep Reinforcement Learning algorithms

This repository contains notebook implementations of the following Neural Process variants: Conditional Neural Processes (CNPs), Neural Processes (NPs), Attentive Neural Processes (ANPs).

Spearmint Bayesian optimization codebase

Heterogeneous Multi-output Gaussian Processes

MATLAB implementation of my Bayesian Optimization algorithms

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKT…

Atari - Deep Reinforcement Learning algorithms in TensorFlow

A TensorFlow implementation of Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures.