This repository contains an implementation of the RL Algorithm Proximal Policy Optimization. The implementation is based on the paper Proximal Policy Optimization Algorithms by Schulman et al. and is ...

By leveraging the camera's visual inputs, the robot gains a more comprehensive understanding of its surroundings and can make informed decisions to navigate safely. The repository includes the ...

Abstract: Reinforcement learning has great potential to solve robotic controlling tasks for different environments. Proximal policy optimization (PPO) is one of the most efficient algorithms of ...

An end-to-end control of the mobile robot is realized by proximal policy optimization learning algorithm. The control policy based on reinforcement learning can more efficiently calculate the actions ...

The performance comparison of three state-of-the-art Reinforcement algorithms, namely the Proximal Policy Optimization (PPO), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft ...

Policy gradient methods are a class of RL algorithms that optimize the agent's policy, which is a function that maps states to actions. Proximal policy optimization (PPO) is a popular and ...