9 citations · 12 across the 6 of their papers we have counts for
6 papers
Neural Weight Search for Scalable Task Incremental Learning
Jian Jiang, Oya Celiktutan
Task incremental learning aims to enable a system to maintain its performance on previously learned tasks while learning new tasks, solving the problem of catastrophic forgetting.…
Policy Gradient With Serial Markov Chain Reasoning
Edoardo Cetin, Oya Celiktutan
We introduce a new framework that performs decision-making in reinforcement learning (RL) as an iterative reasoning process. We model agent behavior as the steady-state distributio…
Stabilizing Off-Policy Deep Reinforcement Learning from Pixels
Edoardo Cetin, Philip J. Ball, Steve Roberts +1
Off-policy reinforcement learning (RL) from pixel observations is notoriously unstable. As a result, many successful algorithms must combine different domain-specific practices and…
Learning Pessimism for Robust and Efficient Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
Off-policy deep reinforcement learning algorithms commonly compensate for overestimation bias during temporal-difference learning by utilizing pessimistic estimates of the expected…
Learning Routines for Effective Off-Policy Reinforcement Learning
Edoardo Cetin, Oya Celiktutan
The performance of reinforcement learning depends upon designing an appropriate action space, where the effect of each action is measurable, yet, granular enough to permit flexible…
IB-DRR: Incremental Learning with Information-Back Discrete Representation Replay
Jian Jiang, Edoardo Cetin, Oya Celiktutan
Incremental learning aims to enable machine learning models to continuously acquire new knowledge given new classes, while maintaining the knowledge already learned for old classes…