Search Results for author:
Found 9 papers, 6 papers with code
Task-conditioned adaptation of visual features in multi-task policy learning
We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that, compared to existing work, it can be addressed with a single policy.
AutoNeRF: Training Implicit Scene Representations with Autonomous Agents
Empirical results show that NeRFs can be trained on actively collected data using just a single episode of experience in an unseen environment, and can be used for several downstream robotic tasks, and that modular trained exploration models outperform other classical and end-to-end baselines.
Multi-Object Navigation with dynamically learned neural implicit representations
Understanding and mapping a new environment are core abilities of any autonomously navigating agent.
Graph augmented Deep Reinforcement Learning in the GameRLand3D environment
We address planning and navigation in challenging 3D video games featuring maps with disconnected regions reachable by agents using special actions.
Godot Reinforcement Learning Agents
We present Godot Reinforcement Learning (RL) Agents, an open-source interface for developing environments and agents in the Godot Game Engine.
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation
In the context of visual navigation, the capacity to map a novel environment is necessary for an agent to exploit its observation history in the considered place and efficiently reach known goals.
Learning to plan with uncertain topological maps
We train an agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local policy.
EgoMap: Projective mapping and structured egocentric memory for Deep RL
The EgoMap architecture incorporates several inductive biases including a differentiable inverse projection of CNN feature vectors onto a top-down spatially structured map.
Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer
In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations.
