Search Results for author:
Found 6 papers, 1 papers with code
The SwaNNFlight System: On-the-Fly Sim-to-Real Adaptation via Anchored Learning
And (iii) anchor critics to help stabilize the fine-tuning of agents during sim-to-real transfer, online learning from real data while retaining behavior optimized in simulation.
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning
Advancements in additive manufacturing have enabled design and fabrication of materials and structures not previously realizable.
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style
MultiCriticAL is tested in the context of multi-style learning, a special case of MTRL where agents are trained to behave with different distinct behavior styles, and yields up to 45% performance gains over the single-critic baselines and even successfully learns behavior styles in cases where single-critic approaches may simply fail to learn.
Honey, I Shrunk The Actor: A Case Study on Preserving Performance with Smaller Actors in Actor-Critic RL
Actors and critics in actor-critic reinforcement learning algorithms are functionally separate, yet they often use the same network architectures.
Regularizing Action Policies for Smooth Control with Reinforcement Learning
A critical problem with the practical utility of controllers trained with deep Reinforcement Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies.
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning
We propose a novel method to exploit this observation to develop robust actor policies, by automatically developing a sampling curriculum over environment settings to use in training.
