680 citations · 725 across the 8 of their papers we have counts for
6 papers · 1 filter
Work in Progress: Temporally Extended Auxiliary Tasks
Craig Sherstan, Bilal Kartal, Pablo Hernandez-Leal +1
Predictive auxiliary tasks have been shown to improve performance in numerous reinforcement learning works, however, this effect is still not well understood. The primary purpose o…
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman
Chao Gao, Bilal Kartal, Pablo Hernandez-Leal +1
How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the r…
Action Guidance with MCTS for Deep Reinforcement Learning
Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning has achieved great successes in recent years, however, one main challenge is the sample inefficiency. In this paper, we focus on how to use action guida…
Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning
Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning has achieved great successes in recent years, but there are still open challenges, such as convergence to locally optimal policies and sample inefficien…
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Bilal Kartal, Pablo Hernandez-Leal, Chao Gao +1
Safe reinforcement learning has many variants and it is still an open research problem. Here, we focus on how to use action guidance by means of a non-expert demonstrator to avoid…
Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL
Bilal Kartal, Pablo Hernandez-Leal, Matthew E. Taylor
Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power. However, there are still several challenges…