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Found 10 papers, 7 papers with code
MaestroMotif: Skill Design from Artificial Intelligence Feedback
Describing skills in natural language has the potential to provide an accessible way to inject human knowledge about decision-making into an AI system.
On the Modeling Capabilities of Large Language Models for Sequential Decision Making
Large pretrained models are showing increasingly better performance in reasoning and planning tasks across different modalities, opening the possibility to leverage them for complex sequential decision making problems.
Code as Reward: Empowering Reinforcement Learning with VLMs
Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion.
Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Exploring rich environments and evaluating one's actions without prior knowledge is immensely challenging.
Deep Laplacian-based Options for Temporally-Extended Exploration
In this paper we address these limitations and show how recent results for directly approximating the eigenfunctions of the Laplacian can be leveraged to truly scale up options-based exploration.
Flexible Option Learning
Temporal abstraction in reinforcement learning (RL), offers the promise of improving generalization and knowledge transfer in complex environments, by propagating information more efficiently over time.
Deep Reinforcement Learning
Hierarchical Reinforcement Learning
+3
Reward Propagation Using Graph Convolutional Networks
Potential-based reward shaping provides an approach for designing good reward functions, with the purpose of speeding up learning.
Options of Interest: Temporal Abstraction with Interest Functions
Temporal abstraction refers to the ability of an agent to use behaviours of controllers which act for a limited, variable amount of time.
Learnings Options End-to-End for Continuous Action Tasks
We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]).
When Waiting is not an Option : Learning Options with a Deliberation Cost
Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance.
