DLR RM | Smooth Exploration for Robotic Reinforcement Learning @DLRRMC | Uploaded 2 years ago | Updated 9 minutes ago
The video presents real robot experiments from our paper at CoRL 2021: "Smooth Exploration for Robotic Reinforcement Learning" by Antonin Raffin, Jens Kober and Freek Stulp.
Paper: openreview.net/forum?id=TSuSGVkjuXd
Code: github.com/DLR-RM/stable-baselines3
Experiments: github.com/DLR-RM/rl-baselines3-zoo
Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploration, or even damage to the robot. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms. To enable this adaptation, we propose two extensions to the original SDE, using more general features and re-sampling the noise periodically, which leads to a new exploration method generalized state-dependent exploration (gSDE). We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car. The noise sampling interval of gSDE enables a compromise between performance and smoothness, which allows training directly on the real robots without loss of performance.
DLR (CC-BY 3.0)
The video presents real robot experiments from our paper at CoRL 2021: "Smooth Exploration for Robotic Reinforcement Learning" by Antonin Raffin, Jens Kober and Freek Stulp.
Paper: openreview.net/forum?id=TSuSGVkjuXd
Code: github.com/DLR-RM/stable-baselines3
Experiments: github.com/DLR-RM/rl-baselines3-zoo
Reinforcement learning (RL) enables robots to learn skills from interactions with the real world. In practice, the unstructured step-based exploration used in Deep RL -- often very successful in simulation -- leads to jerky motion patterns on real robots. Consequences of the resulting shaky behavior are poor exploration, or even damage to the robot. We address these issues by adapting state-dependent exploration (SDE) to current Deep RL algorithms. To enable this adaptation, we propose two extensions to the original SDE, using more general features and re-sampling the noise periodically, which leads to a new exploration method generalized state-dependent exploration (gSDE). We evaluate gSDE both in simulation, on PyBullet continuous control tasks, and directly on three different real robots: a tendon-driven elastic robot, a quadruped and an RC car. The noise sampling interval of gSDE enables a compromise between performance and smoothness, which allows training directly on the real robots without loss of performance.
DLR (CC-BY 3.0)
