The role of lifelong machine learning in bridging the gap between human and machine learning: A scientometric analysis
Due to advancements in data collection, storage, and processing techniques, machine
learning has become a thriving and dominant paradigm. However, one of its main …
learning has become a thriving and dominant paradigm. However, one of its main …
Parameterizing branch-and-bound search trees to learn branching policies
Abstract Branch and Bound (B&B) is the exact tree search method typically used to solve
Mixed-Integer Linear Programming problems (MILPs). Learning branching policies for MILP …
Mixed-Integer Linear Programming problems (MILPs). Learning branching policies for MILP …
Approximate information state for approximate planning and reinforcement learning in partially observed systems
We propose a theoretical framework for approximate planning and learning in partially
observed systems. Our framework is based on the fundamental notion of information state …
observed systems. Our framework is based on the fundamental notion of information state …
Sequoia: A software framework to unify continual learning research
The field of Continual Learning (CL) seeks to develop algorithms that accumulate
knowledge and skills over time through interaction with non-stationary environments. In …
knowledge and skills over time through interaction with non-stationary environments. In …
Structured cooperative reinforcement learning with time-varying composite action space
In recent years, reinforcement learning has achieved excellent results in low-dimensional
static action spaces such as games and simple robotics. However, the action space is …
static action spaces such as games and simple robotics. However, the action space is …
In-context reinforcement learning for variable action spaces
Recent work has shown that supervised pre-training on learning histories of RL algorithms
results in a model that captures the learning process and is able to improve in-context on …
results in a model that captures the learning process and is able to improve in-context on …
Rewiring neurons in non-stationary environments
The human brain rewires itself for neuroplasticity in the presence of new tasks. We are
inspired to harness this key process in continual reinforcement learning, prioritizing …
inspired to harness this key process in continual reinforcement learning, prioritizing …