dc.contributor.author | Grossman, Lev Jacob | |
dc.date.accessioned | 2020-08-28T10:36:21Z | |
dc.date.created | 2020-05 | |
dc.date.issued | 2020-06-17 | |
dc.date.submitted | 2020 | |
dc.identifier.citation | Grossman, Lev Jacob. 2020. Reinforcement Learning to Enable Robust Robotic Model Predictive Control. Bachelor's thesis, Harvard College. | |
dc.identifier.uri | https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37364714 | * |
dc.description.abstract | Traditional methods of robotic planning and trajectory optimization often break down when environmental conditions change, real-world noise is introduced, or when rewards become sparse. Consequently, much of the work involved in calculating trajectories is done not by algorithms, but by hand: tuning cost functions and engineering rewards. This thesis seeks to minimize this human effort by building on prior work combining both model-based and model- free methods within an actor-critic framework. This specific synergy allows for the automatic learning of cost functions expressive enough to enable robust robotic planning. This thesis proposes a novel algorithm, “Reference-Guided, Value-Based MPC,” which combines model predictive control (MPC) and reinforcement learning (RL) to compute feasible trajectories for a robotic arm. The algorithm does this while 1) achieving an almost 50% higher planning success rate than standard MPC, 2) solving in sparse environments considered unsolvable by current state of the art algorithms, and 3) generalizing its solutions to different environment initializations. | |
dc.description.sponsorship | Computer Science | |
dc.description.sponsorship | Computer Science | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dash.license | LAA | |
dc.title | Reinforcement Learning to Enable Robust Robotic Model Predictive Control | |
dc.type | Thesis or Dissertation | |
dash.depositing.author | Grossman, Lev Jacob | |
dc.date.available | 2020-08-28T10:36:21Z | |
thesis.degree.date | 2020 | |
thesis.degree.grantor | Harvard College | |
thesis.degree.grantor | Harvard College | |
thesis.degree.level | Undergraduate | |
thesis.degree.level | Undergraduate | |
thesis.degree.name | AB | |
thesis.degree.name | AB | |
dc.type.material | text | |
thesis.degree.department | Computer Science | |
thesis.degree.department | Computer Science | |
dash.identifier.vireo | | |
dash.author.email | grossmanlev@gmail.com | |