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Found 6 papers, 3 papers with code
MAMBA: an Effective World Model Approach for Meta-Reinforcement Learning
Meta-reinforcement learning (meta-RL) is a promising framework for tackling challenging domains requiring efficient exploration.
Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions.
Goal-Conditioned Supervised Learning with Sub-Goal Prediction
Based on this idea, we propose Trajectory Iterative Learner (TraIL), an extension of GCSL that further exploits the information in a trajectory, and uses it for learning to predict both actions and sub-goals.
Sub-Goal Trees -- a Framework for Goal-Based Reinforcement Learning
Reinforcement learning (RL), building on Bellman's optimality equation, naturally optimizes for a single goal, yet can be made multi-goal by augmenting the state with the goal.
Sub-Goal Trees -- a Framework for Goal-Directed Trajectory Prediction and Optimization
In such problems, the way we choose to represent a trajectory underlies algorithms for trajectory prediction and optimization.
Harnessing Reinforcement Learning for Neural Motion Planning
We then propose a modification of the popular DDPG RL algorithm that is tailored to motion planning domains, by exploiting the known model in the problem and the set of solved plans in the data.
