6 citations · 20 across the 7 of their papers we have counts for
5 papers · 1 filter
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment
Edward Beeching, Maxim Peter, Philippe Marcotte +4
We address planning and navigation in challenging 3D video games featuring maps with disconnected regions reachable by agents using special actions. In this setting, classical symb…
Godot Reinforcement Learning Agents
Edward Beeching, Jilles Debangoye, Olivier Simonin +1
We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine. The Godot RL Agents interface allows…
Learning to plan with uncertain topological maps
Edward Beeching, Jilles Dibangoye, Olivier Simonin +1
We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy. Our main contribution is a data driven…
EgoMap: Projective mapping and structured egocentric memory for Deep RL
Edward Beeching, Christian Wolf, Jilles Dibangoye +1
Tasks involving localization, memorization and planning in partially observable 3D environments are an ongoing challenge in Deep Reinforcement Learning. We present EgoMap, a spatia…
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
Edward Beeching, Christian Wolf, Jilles Dibangoye +1
An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understandi…