Search Results for author:
Found 9 papers, 3 papers with code
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning
This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observability (non-Markov observations), (2) distal and sparse rewards, (3) variable and hierarchical reward structure, (4) multiple-task generation, (5) variable problem complexity.
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original training context, and systems will instead need to adapt to novel distributions and tasks while deployed.
Lifelong Reinforcement Learning with Modulating Masks
The results suggest that RL with modulating masks is a promising approach to lifelong learning, to the composition of knowledge to learn increasingly complex tasks, and to knowledge reuse for efficient and faster learning.
Concept-modulated model-based offline reinforcement learning for rapid generalization
The robustness of any machine learning solution is fundamentally bound by the data it was trained on.
Model-based Reinforcement Learning
reinforcement-learning
+3
Context Meta-Reinforcement Learning via Neuromodulation
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments.
Lifelong Learning with Sketched Structural Regularization
In practice and due to computational constraints, most SR methods crudely approximate the importance matrix by its diagonal.
Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations
Deep neural networks suffer from the inability to preserve the learned data representation (i. e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training.
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
We sample from this distribution and utilize experience replay to avoid forgetting and simultaneously accumulate new knowledge to the abstract distribution in order to couple the current task with past experience.
Neuromodulated Goal-Driven Perception in Uncertain Domains
In uncertain domains, the goals are often unknown and need to be predicted by the organism or system.
