7 citations · 11 across the 4 of their papers we have counts for
4 papers
Revisiting Mixture Policies in Entropy-Regularized Actor-Critic
Jiamin He, Samuel Neumann, Jincheng Mei +2
Mixture policies theoretically offer greater flexibility than unimodal policies in continuous action reinforcement learning, but the practical benefits of this complexity remain el…
The Cross-environment Hyperparameter Setting Benchmark for Reinforcement Learning
Andrew Patterson, Samuel Neumann, Raksha Kumaraswamy +2
This paper introduces a new empirical methodology, the Cross-environment Hyperparameter Setting Benchmark, that compares RL algorithms across environments using a single hyperparam…
Empirical Design in Reinforcement Learning
Andrew Patterson, Samuel Neumann, Martha White +1
Empirical design in reinforcement learning is no small task. Running good experiments requires attention to detail and at times significant computational resources. While compute r…
Greedy Actor-Critic: A New Conditional Cross-Entropy Method for Policy Improvement
Samuel Neumann, Sungsu Lim, Ajin Joseph +3
Many policy gradient methods are variants of Actor-Critic (AC), where a value function (critic) is learned to facilitate updating the parameterized policy (actor). The update to th…