1 citations · 2 across the 8 of their papers we have counts for
9 papers
ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability
Juarez Monteiro, Nathan Gavenski, Guilherme Lima +3
Reinforcement learning agents operating under partial observability must act on incomplete information, making them natural candidates for guidance from small language models (SLMs…
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
Nathan Gavenski, Juarez Monteiro, Francisco Galuppo +2
Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation. We propose Plan, Align, Commit, Think (PACT), a hybrid archit…
When to ASK: Uncertainty-Gated Language Assistance for Reinforcement Learning
Juarez Monteiro, Nathan Gavenski, Gianlucca Zuin +1
Reinforcement learning (RL) agents often struggle with out-of-distribution (OOD) scenarios, leading to high uncertainty and random behavior. While language models (LMs) contain val…
Explorative Imitation Learning: A Path Signature Approach for Continuous Environments
Nathan Gavenski, Juarez Monteiro, Felipe Meneguzzi +2
Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to…
Self-Supervised Adversarial Imitation Learning
Juarez Monteiro, Nathan Gavenski, Felipe Meneguzzi +1
Behavioural cloning is an imitation learning technique that teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unl…
Imitating Unknown Policies via Exploration
Nathan Gavenski, Juarez Monteiro, Roger Granada +2
Behavioral cloning is an imitation learning technique that teaches an agent how to behave through expert demonstrations. Recent approaches use self-supervision of fully-observable…