We study the link between generalization and interference in temporal-difference (TD)
learning. Interference is defined as the inner product of two different gradients, representing …

This research paper brings together many different aspects of the current research on
several fields associated to Reinforcement Learning which has been growing rapidly …

The exploration-exploitation trade-off is at the heart of reinforcement learning (RL). However,
most continuous control benchmarks used in recent RL research only require local …

We consider the core reinforcement-learning problem of on-policy value function
approximation from a batch of trajectory data, and focus on various issues of Temporal …

This thesis investigates the use of bootstrap** in Temporal Difference (TD) learning, a
central mechanism in reinforcement learning (RL), when applied to deep neural networks. I …

Рассматривается решение игровых задач на примере задачи поиска пути в лабиринте
при помощи нейронной сети. Такая задача может быть решена одним из …

This thesis seeks to extend the capabilities of state representation learning (SRL) to help
scale deep reinforcement learning (DRL) algorithms to continuous control tasks with high …

Motion planning is able to solve robotics problems much quicker than any reinforcement
learning algorithm by efficiently searching for a viable trajectory. Indeed, while the main …