Drlnd Continuous Control

Introduce the Udacity Deep Reinforcement Learning Project 2:

This video discusses the paper

In this Webinar we cover

Udacity

In this project, we work with Reacher ...

In this project a

This is the second project of the Deep Reinforcement Learning Nanodegree. I trained a DDPG Agent to solve the Reacher ...

In this project an agent is a two link arm wich end effector will be track an spherical volume in the space.

Deep RL:

Code and Dissertation Document at: https://github.com/david1309/Multi_Task_RL Multi Domain and Multi Task Deep ...

End-to-End Reinforcement Learning for Multi-Agent Continuous Control

This is the result of training a double joint robot arm to reach its target location using DDPG(https://arxiv.org/pdf/1509.02971) ...

Proximal Policy Optimization for

Code + Explanation : https://github.com/antoniopenta/deep_reinforcement_learning/tree/master/drl_continous_control.

This clip shows how a multi-agent, Actor-Critic neural architecture can be used to

Udacity Deep Reinforcement Learning Nanodegree Project 2 Using Deep Deterministic Policy Gradient (DDPG) Method, ...

https://arxiv.org/pdf/1509.02971.pdf.

Comparisons demonstrating the qualitative difference between an experimental

REINFORCE with OpenAi Gym's Cartpole environment walkthrough.