Efficient RL | TransferLab — appliedAI Institute

Efficient RL

Recent and popular advances in Reinforcement Learning are known to be data-hungry. Attempts to handle this deficit include developing complex simulators while improving the sim2real transition of models, explicitly modelling the environment, or leveraging available offline data.

Research feed

Exploiting past success in Off-Policy Actor-Critic

Extracting knowledge from previously gathered data is the very core of exploitation in reinforcement learning (RL). During training, a …

Jump-Start Reinforcement Learning

Reinforcement learning often presents a challenge when training an agent from scratch, as reward sparsity and exploration issues can impede …

Implicit Q Learning

A core challenge when applying dynamic programming based approaches to offline reinforcement learning is the bootstrapping error. This pill …

Safe and efficient deep reinforcement learning

This 2-day dive into deep RL for decision and control is suitable for engineers who want to solve real-world control problems with efficient …

Classical and modern methods in planning and control

A review of model-based planning and control for engineers interested in solving real-world decision and control problems with efficient …

Advantage-Induced Policy Alignment

Building on the classic results on reward weighted regression and its more recent adaptation to deep learning, a new algorithm called …

Critic Regularized Regression

A simple but powerful algorithm for offline reinforcement learning, which can be seen as a combination of behavior cloning and Q-learning, …

Deep Reinforcement Learning at the Edge of the Statistical Precipice

A sound statistical analysis of the performance of RL algorithms is performed, with outcomes that force a reconsideration of the ranking of …

Planning with Diffusion for Flexible Behavior Synthesis

Diffusion models are finding their way into more machine learning domains. This paper introduces a framework for applying them to …

Natural, Trust Region and Proximal Policy Optimization

We present an overview of the theory behind three popular and related algorithms for gradient based policy optimization: natural policy …

Problems and solutions in offline learning

Current reinforcement learning algorithms require tons of data to solve new tasks. This “hunger for data” is especially onerous in high …

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

Paper abstract: Model-free deep reinforcement learning algorithms have been shown to be capable of learning a wide range of robotic skills, …

Other series in Efficient Machine Learning

Data valuation

Attributions of value to training samples can be used to examine data, improve data acquisition, debug and improve models or compensate data …

Data efficiency

Numerous applications of Machine Learning in business intelligence, process optimization, product development or sales do not benefit from …