5 papers
V-Simba: Unleashing the Architectural Potential of RL in Visual Continuous Control
Donghu Kim, Youngdo Lee, Hojoon Lee +6
Improving sample efficiency remains a core challenge in reinforcement learning (RL), especially in real-world settings like robotics, where data collection is costly. This challeng…
Preventing Learning Stagnation in PPO by Scaling to 1 Million Parallel Environments
Michael Beukman, Khimya Khetarpal, Zeyu Zheng +4
An agent's performance stagnating at a suboptimal level is a common problem in deep on-policy RL. Focusing on PPO, we show that plateaus in certain regimes arise not because of kno…
Balancing Expressivity and Robustness: Constrained Rational Activations for Reinforcement Learning
RafaÅ Surdej, MichaÅ Bortkiewicz, Alex Lewandowski +2
Trainable activation functions, whose parameters are optimized alongside network weights, offer increased expressivity compared to fixed activation functions. Specifically, trainab…
Frequency and Generalisation of Periodic Activation Functions in Reinforcement Learning
Augustine N. Mavor-Parker, Matthew J. Sargent, Caswell Barry +2
Periodic activation functions, often referred to as learned Fourier features have been widely demonstrated to improve sample efficiency and stability in a variety of deep RL algori…
Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks
Hojoon Lee, Hyeonseo Cho, Hyunseung Kim +4
This study investigates the loss of generalization ability in neural networks, revisiting warm-starting experiments from Ash & Adams. Our empirical analysis reveals that common met…