Search Results for author:
Found 18 papers, 8 papers with code
AutoGuide: Automated Generation and Selection of State-Aware Guidelines for Large Language Model Agents
The primary limitation of large language models (LLMs) is their restricted understanding of the world.
TOD-Flow: Modeling the Structure of Task-Oriented Dialogues
Our TOD-Flow graph learns what a model can, should, and should not predict, effectively reducing the search space and providing a rationale for the model's prediction.
Code Models are Zero-shot Precondition Reasoners
One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point.
MultiPrompter: Cooperative Prompt Optimization with Multi-Agent Reinforcement Learning
Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
From Heuristic to Analytic: Cognitively Motivated Strategies for Coherent Physical Commonsense Reasoning
We incorporate these interlinked dual processes in fine-tuning and in-context learning with PLMs, applying them to two language understanding tasks that require coherent physical commonsense reasoning.
A Picture is Worth a Thousand Words: Language Models Plan from Pixels
Planning is an important capability of artificial agents that perform long-horizon tasks in real-world environments.
Unsupervised Task Graph Generation from Instructional Video Transcripts
This work explores the problem of generating task graphs of real-world activities.
Multimodal Subtask Graph Generation from Instructional Videos
Real-world tasks consist of multiple inter-dependent subtasks (e. g., a dirty pan needs to be washed before it can be used for cooking).
Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization
Different from the previous meta-rl methods trying to directly infer the unstructured task embedding, our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks, and use it as a prior to improve the task inference in testing.
Hierarchical Reinforcement Learning
Meta Reinforcement Learning
+2
Learning Parameterized Task Structure for Generalization to Unseen Entities
These subtasks are defined in terms of entities (e. g., "apple", "pear") that can be recombined to form new subtasks (e. g., "pickup apple", and "pickup pear").
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
SFL leverages the ability of successor features (SF) to capture transition dynamics, using it to drive exploration by estimating state-novelty and to enable high-level planning by abstracting the state-space as a non-parametric landmark-based graph.
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on the agent's trajectory that improves the sample efficiency in sparse-reward MDPs.
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment
In this work, we show that one can learn object-interaction tasks from scratch without supervision by learning an attentive object-model as an auxiliary task during task learning with an object-centric relational RL agent.
BRPO: Batch Residual Policy Optimization
In batch reinforcement learning (RL), one often constrains a learned policy to be close to the behavior (data-generating) policy, e. g., by constraining the learned action distribution to differ from the behavior policy by some maximum degree that is the same at each state.
Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
We propose and address a novel few-shot RL problem, where a task is characterized by a subtask graph which describes a set of subtasks and their dependencies that are unknown to the agent.
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
We introduce a new RL problem where the agent is required to generalize to a previously-unseen environment characterized by a subtask graph which describes a set of subtasks and their dependencies.
Neural Task Graph Execution
Unlike existing approaches which explicitly describe what the agent should do, our problem only describes properties of subtasks and relationships between them, which requires the agent to perform a complex reasoning to find the optimal subtask to execute.
Learning to Generate Long-term Future via Hierarchical Prediction
To avoid inherent compounding errors in recursive pixel-level prediction, we propose to first estimate high-level structure in the input frames, then predict how that structure evolves in the future, and finally by observing a single frame from the past and the predicted high-level structure, we construct the future frames without having to observe any of the pixel-level predictions.
