1 citations · 1 across the 4 of their papers we have counts for
4 papers
Adam on Local Time: Addressing Nonstationarity in RL with Relative Adam Timesteps
Benjamin Ellis, Matthew T. Jackson, Andrei Lupu +4
In reinforcement learning (RL), it is common to apply techniques used broadly in machine learning such as neural network function approximators and momentum-based optimizers. Howev…
Beyond the Boundaries of Proximal Policy Optimization
Charlie B. Tan, Edan Toledo, Benjamin Ellis +2
Proximal policy optimization (PPO) is a widely-used algorithm for on-policy reinforcement learning. This work offers an alternative perspective of PPO, in which it is decomposed in…
Policy-Guided Diffusion
Matthew Thomas Jackson, Michael Tryfan Matthews, Cong Lu +3
In many real-world settings, agents must learn from an offline dataset gathered by some prior behavior policy. Such a setting naturally leads to distribution shift between the beha…
Trust-Region-Free Policy Optimization for Stochastic Policies
Mingfei Sun, Benjamin Ellis, Anuj Mahajan +3
Trust Region Policy Optimization (TRPO) is an iterative method that simultaneously maximizes a surrogate objective and enforces a trust region constraint over consecutive policies…