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Found 20 papers, 6 papers with code
EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data
Prior work in skill-based RL either requires expert supervision to define useful skills, which is hard to scale, or learns a skill-space from offline data with heuristics that limit the adaptability of the skills, making them difficult to transfer during downstream RL.
RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback
Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions.
LiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback.
Bootstrap Your Own Skills: Learning to Solve New Tasks with Large Language Model Guidance
Instead, our approach BOSS (BOotStrapping your own Skills) learns to accomplish new tasks by performing "skill bootstrapping," where an agent with a set of primitive skills interacts with the environment to practice new skills without receiving reward feedback for tasks outside of the initial skill set.
TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models
Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e. g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data.
SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling
Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks.
Hierarchical Neural Program Synthesis
Then, we train a high-level module to comprehend the task specification (e. g., input/output pairs or demonstrations) from long programs and produce a sequence of task embeddings, which are then decoded by the program decoder and composed to yield the synthesized program.
Learning to Synthesize Programs as Interpretable and Generalizable Policies
To alleviate the difficulty of learning to compose programs to induce the desired agent behavior from scratch, we propose to first learn a program embedding space that continuously parameterizes diverse behaviors in an unsupervised manner and then search over the learned program embedding space to yield a program that maximizes the return for a given task.
TRECVID 2020: A comprehensive campaign for evaluating video retrieval tasks across multiple application domains
In total, 29 teams from various research organizations worldwide completed one or more of the following six tasks: 1.
Minimum Description Length Skills for Accelerated Reinforcement Learning
Humans can quickly learn new tasks by reusing a large number of previously acquired skills.
Hierarchical Reinforcement Learning By Discovering Intrinsic Options
We propose a hierarchical reinforcement learning method, HIDIO, that can learn task-agnostic options in a self-supervised manner while jointly learning to utilize them to solve sparse-reward tasks.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
COG: Connecting New Skills to Past Experience with Offline Reinforcement Learning
Reinforcement learning has been applied to a wide variety of robotics problems, but most of such applications involve collecting data from scratch for each new task.
TRECVID 2019: An Evaluation Campaign to Benchmark Video Activity Detection, Video Captioning and Matching, and Video Search & Retrieval
The TREC Video Retrieval Evaluation (TRECVID) 2019 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in research and development of content-based exploitation and retrieval of information from digital video via open, metrics-based evaluation.
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Reinforcement learning (RL) in real-world safety-critical target settings like urban driving is hazardous, imperiling the RL agent, other agents, and the environment.
Unsupervised Projection Networks for Generative Adversarial Networks
We propose the use of unsupervised learning to train projection networks that project onto the latent space of an already trained generator.
Hope For The Best But Prepare For The Worst: Cautious Adaptation In RL Agents
We study the problem of safe adaptation: given a model trained on a variety of past experiences for some task, can this model learn to perform that task in a new situation while avoiding catastrophic failure?
REPLAB: A Reproducible Low-Cost Arm Benchmark Platform for Robotic Learning
We envision REPLAB as a framework for reproducible research across manipulation tasks, and as a step in this direction, we define a template for a grasping benchmark consisting of a task definition, evaluation protocol, performance measures, and a dataset of 92k grasp attempts.
Porcupine Neural Networks: Approximating Neural Network Landscapes
Neural networks have been used prominently in several machine learning and statistics applications.
A Spectral Approach to Generalization and Optimization in Neural Networks
The recent success of deep neural networks stems from their ability to generalize well on real data; however, Zhang et al. have observed that neural networks can easily overfit random labels.
Porcupine Neural Networks: (Almost) All Local Optima are Global
Neural networks have been used prominently in several machine learning and statistics applications.
