Training a Generally Curious Agent
- URL: http://arxiv.org/abs/2502.17543v2
- Date: Wed, 05 Mar 2025 06:53:52 GMT
- Title: Training a Generally Curious Agent
- Authors: Fahim Tajwar, Yiding Jiang, Abitha Thankaraj, Sumaita Sadia Rahman, J Zico Kolter, Jeff Schneider, Ruslan Salakhutdinov,
- Abstract summary: We present PAPRIKA, a fine-tuning approach that enables language models to develop general decision-making capabilities.<n> Experimental results show that models fine-tuned with PAPRIKA can effectively transfer their learned decision-making capabilities to entirely unseen tasks.<n>These results suggest a promising path towards AI systems that can autonomously solve novel sequential decision-making problems.
- Score: 86.84089201249104
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Efficient exploration is essential for intelligent systems interacting with their environment, but existing language models often fall short in scenarios that require strategic information gathering. In this paper, we present PAPRIKA, a fine-tuning approach that enables language models to develop general decision-making capabilities that are not confined to particular environments. By training on synthetic interaction data from different tasks that require diverse strategies, PAPRIKA teaches models to explore and adapt their behavior on a new task based on environment feedback in-context without more gradient updates. Experimental results show that models fine-tuned with PAPRIKA can effectively transfer their learned decision-making capabilities to entirely unseen tasks without additional training. Unlike traditional training, our approach's primary bottleneck lies in sampling useful interaction data instead of model updates. To improve sample efficiency, we propose a curriculum learning strategy that prioritizes sampling trajectories from tasks with high learning potential. These results suggest a promising path towards AI systems that can autonomously solve novel sequential decision-making problems that require interactions with the external world.
Related papers
- Scalable Language Model with Generalized Continual Learning [58.700439919096155]
The Joint Adaptive Re-ization (JARe) is integrated with Dynamic Task-related Knowledge Retrieval (DTKR) to enable adaptive adjustment of language models based on specific downstream tasks.
Our method demonstrates state-of-the-art performance on diverse backbones and benchmarks, achieving effective continual learning in both full-set and few-shot scenarios with minimal forgetting.
arXiv Detail & Related papers (2024-04-11T04:22:15Z) - Self-Supervised Reinforcement Learning that Transfers using Random
Features [41.00256493388967]
We propose a self-supervised reinforcement learning method that enables the transfer of behaviors across tasks with different rewards.
Our method is self-supervised in that it can be trained on offline datasets without reward labels, but can then be quickly deployed on new tasks.
arXiv Detail & Related papers (2023-05-26T20:37:06Z) - Investigating the role of model-based learning in exploration and
transfer [11.652741003589027]
In this paper, we investigate transfer learning in the context of model-based agents.
We find that a model-based approach outperforms controlled model-free baselines for transfer learning.
Our results show that intrinsic exploration combined with environment models present a viable direction towards agents that are self-supervised and able to generalize to novel reward functions.
arXiv Detail & Related papers (2023-02-08T11:49:58Z) - Visual-Language Navigation Pretraining via Prompt-based Environmental
Self-exploration [83.96729205383501]
We introduce prompt-based learning to achieve fast adaptation for language embeddings.
Our model can adapt to diverse vision-language navigation tasks, including VLN and REVERIE.
arXiv Detail & Related papers (2022-03-08T11:01:24Z) - Double Meta-Learning for Data Efficient Policy Optimization in
Non-Stationary Environments [12.45281856559346]
We are interested in learning models of non-stationary environments, which can be framed as a multi-task learning problem.
Model-free reinforcement learning algorithms can achieve good performance in multi-task learning at a cost of extensive sampling.
While model-based approaches are among the most data efficient learning algorithms, they still struggle with complex tasks and model uncertainties.
arXiv Detail & Related papers (2020-11-21T03:19:35Z) - Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials.
We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
arXiv Detail & Related papers (2020-11-19T18:47:40Z) - Importance Weighted Policy Learning and Adaptation [89.46467771037054]
We study a complementary approach which is conceptually simple, general, modular and built on top of recent improvements in off-policy learning.
The framework is inspired by ideas from the probabilistic inference literature and combines robust off-policy learning with a behavior prior.
Our approach achieves competitive adaptation performance on hold-out tasks compared to meta reinforcement learning baselines and can scale to complex sparse-reward scenarios.
arXiv Detail & Related papers (2020-09-10T14:16:58Z) - Pre-trained Word Embeddings for Goal-conditional Transfer Learning in
Reinforcement Learning [0.0]
We show how a pre-trained task-independent language model can make a goal-conditional RL agent more sample efficient.
We do this by facilitating transfer learning between different related tasks.
arXiv Detail & Related papers (2020-07-10T06:42:00Z) - Guided Uncertainty-Aware Policy Optimization: Combining Learning and
Model-Based Strategies for Sample-Efficient Policy Learning [75.56839075060819]
Traditional robotic approaches rely on an accurate model of the environment, a detailed description of how to perform the task, and a robust perception system to keep track of the current state.
reinforcement learning approaches can operate directly from raw sensory inputs with only a reward signal to describe the task, but are extremely sample-inefficient and brittle.
In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline.
arXiv Detail & Related papers (2020-05-21T19:47:05Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.