187 citations · 331 across the 16 of their papers we have counts for
9 papers · 1 filter
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
Riashat Islam, Manan Tomar, Alex Lamb +8
Learning to control an agent from data collected offline in a rich pixel-based visual observation space is vital for real-world applications of reinforcement learning (RL). A major…
Modular Lifelong Reinforcement Learning via Neural Composition
Jorge A. Mendez, Harm van Seijen, Eric Eaton
Humans commonly solve complex problems by decomposing them into easier subproblems and then combining the subproblem solutions. This type of compositional reasoning permits reuse o…
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
Sungryull Sohn, Sungtae Lee, Jongwook Choi +3
We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent's trajectory that improves the sample efficiency in sparse-reward MDPs. We show that any optimal p…
The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning
Harm van Seijen, Hadi Nekoei, Evan Racah +1
Deep model-based Reinforcement Learning (RL) has the potential to substantially improve the sample-efficiency of deep RL. While various challenges have long held it back, a number…
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Arash Tavakoli
In an effort to better understand the different ways in which the discount factor affects the optimization process in reinforcement learning, we designed a set of experiments to st…
Learning Invariances for Policy Generalization
Remi Tachet, Philip Bachman, Harm van Seijen
While recent progress has spawned very powerful machine learning systems, those agents remain extremely specialized and fail to transfer the knowledge they gain to similar yet unse…
Hybrid Reward Architecture for Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff +3
One of the main challenges in reinforcement learning (RL) is generalisation. In typical deep RL methods this is achieved by approximating the optimal value function with a low-dime…
Multi-Advisor Reinforcement Learning
Romain Laroche, Mehdi Fatemi, Joshua Romoff +1
We consider tackling a single-agent RL problem by distributing it to learners. These learners, called advisors, endeavour to solve the problem from a different focus. Their adv…
Separation of Concerns in Reinforcement Learning
Harm van Seijen, Mehdi Fatemi, Joshua Romoff +1
In this paper, we propose a framework for solving a single-agent task by using multiple agents, each focusing on different aspects of the task. This approach has two main advantage…