Search Results for author:
Found 5 papers, 3 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.
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
Using an interactive textual environment designed to study higher-level forms of functional grounding, and a set of spatial and navigation tasks, we study several scientific questions: 1) Can LLMs boost sample efficiency for online learning of various RL tasks?
TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RL
Training autonomous agents able to generalize to multiple tasks is a key target of Deep Reinforcement Learning (DRL) research.
Meta Automatic Curriculum Learning
In such complex task spaces, it is essential to rely on some form of Automatic Curriculum Learning (ACL) to adapt the task sampling distribution to a given learning agent, instead of randomly sampling tasks, as many could end up being either trivial or unfeasible.
Deep Recurrent Q-Learning vs Deep Q-Learning on a simple Partially Observable Markov Decision Process with Minecraft
Deep Q-Learning has been successfully applied to a wide variety of tasks in the past several years.
