6 votes
Accepted

What is the definition of `rollout' in neural network or OpenAI gym

The standard use of “rollout” (also called a “playout”) is in regard to an execution of a policy from the current state when there is some uncertainty about the next state or outcome - it is one ...
5 votes

What is the definition of `rollout' in neural network or OpenAI gym

The definition of "rollouts" given by Planning chemical syntheses with deep neural networks and symbolic AI (Segler, Preuss & Waller ; doi: 10.1038/nature25978 ; credit to jsotola): Rollouts ...
3 votes

Applications of Reinforcement Learning

It's true that using RL in robotics involves many challenges, including the usually high dimensionality of problem spaces, the cost and limitations of real-world sessions, the impossibility or ...
  • 1,286
3 votes

PID tuning with (Deep) Reinforcement Learning

Many reainforcement learning methods require descrete actions. As you indentified, increasing and decreasing the values is one option. If it is an adaptive PID, then it might take some time to ...
  • 6,507
3 votes
Accepted

PID tuning with (Deep) Reinforcement Learning

I read a bit more and realized that in RL states and rewards accept a wide variety of interpretations and this is the real complexity nowadays of this learning problem. In case of PID values, problem ...
  • 460
2 votes

What are myopic and non-myopic policies?

A myopic policy is one that simply maximises the average immediate reward. It is "myopic" in the sense that it only considers the single criterion. It has the advantage of being relatively easy to ...
  • 1,019
2 votes
Accepted

Reinforcement Learning in global and local path planning for mobile robots and self-driving car

No, this is not applicable for a car, it is just an introductory, extremely simplified example. It is one step closer to a mobile robot, then to a car, at least a mobile robot (at least some of them) ...
  • 6,507
1 vote

Reinforcement Learning in global and local path planning for mobile robots and self-driving car

1- No it is not. This example is the beginning of RL, while Self-driving cars are way much complex. In a simplified view, there are two main differences. state and action in the shown example are ...
1 vote

Having Issues Importing and Using RLGlue locally with Python For Reinforcement Learning

This worked for me: from RLGlue.rl_glue import RLGlue This is only with the Coursera version of RLGlue
1 vote

Reinforcement train butterfly robot in virtual reality?

Unreal, Unity and other game engines, Gazebo, Mujoco and other Physics engines are good at simulating multi body dynamics. There is no deep conceptual difference between them. You can use whichever ...
  • 6,507
1 vote

What type of rigid body rotation can best be learned by neural networks?

The main problem is the continuity of the representation. This paper explains it.
  • 6,507
1 vote

What type of rigid body rotation can best be learned by neural networks?

Every three-dimensional parameterization of rotations has singularity. So even if you would implement the kinematics directly you would still run into trouble for some rotations when using Euler ...
  • 941
1 vote

What are myopic and non-myopic policies?

You first have to be clear about the core RL terms, to understand myopic and non-myopic policies: Policy: Suppose each cell in the grid below is a state that the RL agent can be in. From each state, ...
  • 143
1 vote

Tuning Line follower PID constants with Q-learning

I am currently working on a very similar project, the only difference is that I am using a simulation package (MATLAB Simmechanics) where I have modeled a mobile robot with 2 actuated wheels and a ...
  • 244
1 vote
Accepted

Has hierarchical learning been embodied in a robot before?

HRL has been embodied in a robot in multiple cases. In a reaching, shelving robot. In a robot learning how to stand-up. In robot navigation. However, how HRL applied in each of these cases varies. ...
  • 161

Only top scored, non community-wiki answers of a minimum length are eligible