98 citations · 246 across the 18 of their papers we have counts for
9 papers · 1 filter
ReLOAD: Reinforcement Learning with Optimistic Ascent-Descent for Last-Iterate Convergence in Constrained MDPs
Ted Moskovitz, Brendan O'Donoghue, Vivek Veeriah +3
In recent years, Reinforcement Learning (RL) has been applied to real-world problems with increasing success. Such applications often require to put constraints on the agent's beha…
GrASP: Gradient-Based Affordance Selection for Planning
Vivek Veeriah, Zeyu Zheng, Richard Lewis +1
Planning with a learned model is arguably a key component of intelligence. There are several challenges in realizing such a component in large-scale reinforcement learning (RL) pro…
Discovery of Options via Meta-Learned Subgoals
Vivek Veeriah, Tom Zahavy, Matteo Hessel +6
Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster. However, despite prior work on this topic, the problem of disc…
Learning State Representations from Random Deep Action-conditional Predictions
Zeyu Zheng, Vivek Veeriah, Risto Vuorio +2
Our main contribution in this work is an empirical finding that random General Value Functions (GVFs), i.e., deep action-conditional predictions -- random both in what feature of o…
Learning Retrospective Knowledge with Reverse Reinforcement Learning
Shangtong Zhang, Vivek Veeriah, Shimon Whiteson
We present a Reverse Reinforcement Learning (Reverse RL) approach for representing retrospective knowledge. General Value Functions (GVFs) have enjoyed great success in representin…
Learning Feature Relevance Through Step Size Adaptation in Temporal-Difference Learning
Alex Kearney, Vivek Veeriah, Jaden Travnik +2
There is a long history of using meta learning as representation learning, specifically for determining the relevance of inputs. In this paper, we examine an instance of meta-learn…
Many-Goals Reinforcement Learning
Vivek Veeriah, Junhyuk Oh, Satinder Singh
All-goals updating exploits the off-policy nature of Q-learning to update all possible goals an agent could have from each transition in the world, and was introduced into Reinforc…
TIDBD: Adapting Temporal-difference Step-sizes Through Stochastic Meta-descent
Alex Kearney, Vivek Veeriah, Jaden B. Travnik +2
In this paper, we introduce a method for adapting the step-sizes of temporal difference (TD) learning. The performance of TD methods often depends on well chosen step-sizes, yet fe…
Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks
Vivek Veeriah, Shangtong Zhang, Richard S. Sutton
Representations are fundamental to artificial intelligence. The performance of a learning system depends on the type of representation used for representing the data. Typically, th…