Search Results for author:
Found 7 papers, 5 papers with code
Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent
The search for a general model that can operate seamlessly across multiple domains remains a key goal in machine learning research.
Zephyr: Direct Distillation of LM Alignment
Starting from a dataset of outputs ranked by a teacher model, we apply distilled direct preference optimization (dDPO) to learn a chat model with significantly improved intent alignment.
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.
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.
