IEEE

Comparative Analysis of A3C and PPO Algorithms in Reinforcement Learning: A Survey on General Environments

Abstract: This research article presents a comparison between two mainstream Deep Reinforcement Learning (DRL) algorithms, Asynchronous Advantage Actor-Critic (A3C) and Proximal Policy Optimization ...
Frontiers

Solving robotics tasks with prior demonstration via exploration-efficient deep reinforcement learning

This paper proposes an exploration-efficient deep reinforcement learning with reference (DRLR) policy framework for learning robotics tasks incorporating demonstrations. The DRLR framework is ...
Frontiers

LG-H-PPO: offline hierarchical PPO for robot path planning on a latent graph

The path planning capability of autonomous robots in complex environments is crucial for their widespread application in the real world. However, long-term decision-making and sparse reward signals ...
IEEE

Safe Reinforcement Learning via Episodic Control

Abstract: Safe reinforcement learning (Safe RL) aims to learn policies capable of learning and adapting within complex environments while ensuring actions remain free from catastrophic consequences.
ZDNet

True agentic AI is years away - here's why and how we get there

Today's AI agents don't meet the definition of true agents. Key missing elements are reinforcement learning and complex memory. It will take at least five years to get AI agents where they need to be.