Learning techniques is a fundamental challenge with Samuel Cohen*, Brandon Amos*, and Yaron Lipmanīridging logical and algorithmic reasoning with modern machine Simple deterministic world model without requiring Locomotion tasks from the OpenAI gym, including a Model-based methods on the proprioceptive MuJoCo Surpass the asymptotic performance of other Improve the value estimate for the policy update. This SVG variationĬaptures the model-free soft actor-critic method asĪn instance when the model rollout horizon is zero, and otherwise uses short-horizon model rollouts to World model to improve the short-horizon valueĮstimate, and 3) a learned model-free value estimateĪfter the model’s rollout. Help with exploration, 2) a learned deterministic Systems and uses 1) an entropy regularization to Variant of the model-based SVG that scales to larger Well-known family of methods for controllingĬontinuous systems which includes model-basedĪpproaches that distill a model-based valueĮxpansion into a model-free policy. Stochastic value gradient (SVG), which is a Unable to achieve the same asymptotic performance onĬhallenging continuous control tasks due to theĬomplexity of learning and controlling an explicit However, in practice model-based methods are Sample-efficiency in comparison to model-freeĪgents. Knowledge to agents in hopes of improving the Model-based reinforcement learning approaches add explicit domain Ranging from simple rigid transformation of theĮxpert domain to arbitrary transformation of theĢ021 On the model-based stochastic value gradient for continuous reinforcement learningīrandon Amos, Samuel Stanton, Denis Yarats, and Andrew Gordon Wilson GWIL in non-trivial continuous control domains Optimality, revealing its possibilities and Our theory formallyĬharacterizes the scenarios where GWIL preserves To align and compare states between the different Imitation that uses the Gromov-Wasserstein distance Imitation Learning (GWIL), a method for cross-domain Comparing trajectories and stationaryĭistributions between the expert and imitationĪgents is challenging because they live on differentĭimensionality.
AMOS VERSION 21 HOW TO
2022 Cross-Domain Imitation Learning via Optimal TransportĪrnaud Fickinger, Samuel Cohen, Stuart Russell, and Brandon AmosĬross-domain imitation learning studies how to leverage expertĭemonstrations of one agent to train an imitation Representative publications that I am a primary author on are
VT Intelligence Community Conter for Academic Excellence, Salem-Roanoke County Chamber of Commerce, Roanoke County Public Schools Engineering, (Hosts: Misha Denil and Nando de Freitas) in Computer Science, Carnegie Mellon Universityĭifferentiable Optimization-Based Modeling for Machine LearningĪdvisors: J.
AMOS VERSION 21 CODE
I believe that science should be open and reproducible and freely publish my research code to GitHub.Įducation Ph.D. A key theme of my work in this space involves the use of optimization as a differentiable building block in larger architectures that are end-to-end learned. My research is on learning systems that understand and interact with our world and focuses on integrating structural information and domain knowledge into these systems to represent non-trivial reasoning operations. I am a research scientist at Facebook AI (FAIR) in NYC and study foundational topics in machine learning and optimization, recently involving reinforcement learning, control, optimal transport, and geometry.
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