3 citations · 11 across the 9 of their papers we have counts for
6 papers · 1 filter
Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
Shengyi Huang, Quentin Gallouédec, Florian Felten +30
In many Reinforcement Learning (RL) papers, learning curves are useful indicators to measure the effectiveness of RL algorithms. However, the complete raw data of the learning curv…
Adversarial Style Transfer for Robust Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
This paper proposes an algorithm that aims to improve generalization for reinforcement learning agents by removing overfitting to confounding features. Our approach consists of a m…
Adversarial Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
The policy represented by the deep neural network can overfit the spurious features in observations, which hamper a reinforcement learning agent from learning effective policy. Thi…
Robust Policy Optimization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
The policy gradient method enjoys the simplicity of the objective where the agent optimizes the cumulative reward directly. Moreover, in the continuous action domain, parameterized…
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
This paper proposes an advantage estimation approach based on data augmentation for policy optimization. Unlike using data augmentation on the input to learn value and policy funct…
Bootstrap State Representation using Style Transfer for Better Generalization in Deep Reinforcement Learning
Md Masudur Rahman, Yexiang Xue
Deep Reinforcement Learning (RL) agents often overfit the training environment, leading to poor generalization performance. In this paper, we propose Thinker, a bootstrapping metho…