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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 simulation from your current state. The purpose is for an agent to evaluate many possible next actions in order to find an action that will maximize value (long-term ...


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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 perfectly modelling the robot-environment system, and the complexity of reward functions that accurately reflect desired behaviors. That said, a number of approaches ...


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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 are Monte Carlo simulations, in which random search steps are performed without branching until a solution has been found or a maximum depth is reached. These ...


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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 incerase the parameters if you only have an increase by the factor of 0.1. I would recommend more then one increasing factor as possible action. Increase by 0.1, 1, 10,...


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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 can be formulated as the following: imagine a Kp value, it represents a state. Next state could be increase or decrease 0.1. Same with the next state, and so ...


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In short, yes, there are a number of robotics companies catering to just your needs. Specifically which apply to you depends on what type of robot you desire. Naturally cost varies with the complexity and durability. For example if you would be satisfied with a differential drive robot for indoor use then the iRobot Create may suit your needs. If you need ...


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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 you prefer. Flying is not only multi body dynamics but also fluid dynamics. This is extremely hard to simulate accurately. It is usually done with CFD (...


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The main problem is the continuity of the representation. This paper explains it.


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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 angles (or any other three-dimensional parameterization). Quaternions, or more accurately unit quaternions, do not have such singularities. Though, do not have a ...


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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. The first uses HRL to manipulate Dynamic Movement Primitives, while the second, older method focuses moreso on learning state space values.


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You will need MuJoCo if you care about robust physics predictions. For UR5 robot, we have created a model for MuJoCo here. This is, however, a planar robot. We have removed the joints from UR5 and we are using it as a planar robot for now. You can use it as a starting point, however. Another solution is to use OpenRAVE simulator. We wrote a controller for ...


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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 implement. A fairly well-known example is the hill-climbing algorithm. However, a myopic search is particularly vulnerable to becoming trapped at a local optima, ...


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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 castor wheel. I have 4 sensors, as a result, I am not using the "middle position" as a reward but I can easily modify that. My model takes parameters such as ...


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