Loss of plasticity in continual deep reinforcement learning
In this paper, we characterize the behavior of canonical value-based deep reinforcement
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …
Learning dynamics and generalization in reinforcement learning
Solving a reinforcement learning (RL) problem poses two competing challenges: fitting a
potentially discontinuous value function, and generalizing well to new observations. In this …
potentially discontinuous value function, and generalizing well to new observations. In this …
Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn
H Tang, G Berseth - arXiv preprint arXiv:2409.04792, 2024 - arxiv.org
Deep neural networks provide Reinforcement Learning (RL) powerful function
approximators to address large-scale decision-making problems. However, these …
approximators to address large-scale decision-making problems. However, these …