Continuous control with deep reinforcement learning · HackerPapers discussions

Continuous control with deep reinforcement learning — mentioned by laquition

Tue, 01 Dec 2020 18:30:23 GMT

Stagnation is a known problem in reinforcement learning and similar methods. It's very easy to get stuck at a local maximum. My favorite fun example is https://gym.openai.com/envs/BipedalWalkerHardcore-v2/ where a standard DDPG(https://arxiv.org/abs/1509.02971) will get stuck at pits in the environment. Although it could get a higher score if it learned to jump, there is a penalty with falling in that makes it stabilize on standing still and running out the timer. Video: https://www.youtube.com/watch?v=DEGwhjEUFoI I suspect there is something similar going on with video/music recommendations.…

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Continuous control with deep reinforcement learning — mentioned by ogrisel

Wed, 09 Mar 2016 20:26:46 GMT

...and it's possible to train learning agents to sense and interact with a world described by high dimensional continuous vector spaces, for instance using conv nets (for sensing audio / video signals) and actor-critic to learn an continuous policy: http://arxiv.org/abs/1509.02971 The fact that the (reinforcement) learning problem is hard or not is not directly related to whether the observation and action spaces are discrete or continuous.

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Continuous control with deep reinforcement learning — mentioned by jpfr

Wed, 16 Dec 2015 16:15:26 GMT

Prototypical case of the 80/20 rule. He has implemented the happy case. But that system is nothing people realistically would want to drive their cars. What he did is impressive. But the results are not that outlandish for a talented person. 1) Hook up a computer to the CAN-Bus network of the car [1] and attach a bunch of sensor peripherals. 2) Drive around for some time and record everything to disk. 3) Implement some of the recent ideas from deep reinforcement learing [2,3]. For training, feed the system with the oberservations from test drives and reward actions that mimick the reactions…

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Continuous control with deep reinforcement learning — mentioned by mappingbabeljc

Fri, 02 Oct 2015 20:01:39 GMT

It's handy knowing they're fiddling with ROS, and the research papers show use of DNN&RL for robotics via simulated tasks with actions keying off of raw pixel inputs (paper: http://arxiv.org/pdf/1509.02971v1.pdf), and grasp paper shows ambitions on dexterous manipulation. I think valuable to have some idea of the research areas. I'm biased because I wrote the article, though! Aiming to produce a more granular article as soon as I can report it out.

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Continuous control with deep reinforcement learning — HN submission

Fri, 11 Sep 2015 21:31:31 GMT

DeepMind: Continuous control with deep reinforcement learning

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.

Continuous control with deep reinforcement learning — HN submission

Fri, 11 Sep 2015 02:43:51 GMT

Continuous control with deep reinforcement learning

1 point; 0 discussion replies on HN. Paper and source history. A submission is not an endorsement.