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Found 24 papers, 13 papers with code
Stream of Search (SoS): Learning to Search in Language
In this paper, we show how language models can be taught to search by representing the process of search in language, as a flattened string -- a stream of search (SoS).
Yell At Your Robot: Improving On-the-Fly from Language Corrections
In this paper, we make the following observation: high-level policies that index into sufficiently rich and expressive low-level language-conditioned skills can be readily supervised with human feedback in the form of language corrections.
A Critical Evaluation of AI Feedback for Aligning Large Language Models
RLAIF first performs supervised fine-tuning (SFT) using demonstrations from a teacher model and then further fine-tunes the model with reinforcement learning (RL), using feedback from a critic model.
RLVF: Learning from Verbal Feedback without Overgeneralization
The diversity of contexts in which large language models (LLMs) are deployed requires the ability to modify or customize default model behaviors to incorporate nuanced requirements and preferences.
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
We posit that a significant challenge to widespread adoption of robotic RL, as well as further development of robotic RL methods, is the comparative inaccessibility of such methods.
Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
We provide theoretical analysis of our selection mechanism and demonstrate that ROAM enables a robot to adapt rapidly to changes in dynamics both in simulation and on a real Go1 quadruped, even successfully moving forward with roller skates on its feet.
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
We aim to enable this paradigm in robotic reinforcement learning, allowing a robot to learn a new task with little human effort by leveraging data and models from the Internet.
An Emulator for Fine-Tuning Large Language Models using Small Language Models
To aid in doing so, we introduce a novel technique for decoupling the knowledge and skills gained in these two stages, enabling a direct answer to the question, "What would happen if we combined the knowledge learned by a large model during pre-training with the knowledge learned by a small model during fine-tuning (or vice versa)?"
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
Can we leverage offline RL to recover better policies from online interaction?
Waypoint-Based Imitation Learning for Robotic Manipulation
AWE can be combined with any BC algorithm, and we find that AWE can increase the success rate of state-of-the-art algorithms by up to 25% in simulation and by 4-28% on real-world bimanual manipulation tasks, reducing the decision making horizon by up to a factor of 10.
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from human feedback (RLHF).
Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback
A trustworthy real-world prediction system should produce well-calibrated confidence scores; that is, its confidence in an answer should be indicative of the likelihood that the answer is correct, enabling deferral to an expert in cases of low-confidence predictions.
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning
In this work, we propose MEDAL++, a novel design for self-improving robotic systems: given a small set of expert demonstrations at the start, the robot autonomously practices the task by learning to both do and undo the task, simultaneously inferring the reward function from the demonstrations.
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
A long-term goal of reinforcement learning is to design agents that can autonomously interact and learn in the world.
You Only Live Once: Single-Life Reinforcement Learning
We formalize this problem setting, which we call single-life reinforcement learning (SLRL), where an agent must complete a task within a single episode without interventions, utilizing its prior experience while contending with some form of novelty.
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
Prior works have considered an alternating approach where a forward policy learns to solve the task and the backward policy learns to reset the environment, but what initial state distribution should the backward policy reset the agent to?
Autonomous Reinforcement Learning: Formalism and Benchmarking
In this paper, we aim to address this discrepancy by laying out a framework for Autonomous Reinforcement Learning (ARL): reinforcement learning where the agent not only learns through its own experience, but also contends with lack of human supervision to reset between trials.
Autonomous Reinforcement Learning via Subgoal Curricula
Reinforcement learning (RL) promises to enable autonomous acquisition of complex behaviors for diverse agents.
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning
Learning to reach goal states and learning diverse skills through mutual information (MI) maximization have been proposed as principled frameworks for self-supervised reinforcement learning, allowing agents to acquire broadly applicable multitask policies with minimal reward engineering.
Discriminator Augmented Model-Based Reinforcement Learning
By planning through a learned dynamics model, model-based reinforcement learning (MBRL) offers the prospect of good performance with little environment interaction.
Model-based Reinforcement Learning
reinforcement-learning
+1
Dynamics-Aware Unsupervised Skill Discovery
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning
Can we instead develop efficient reinforcement learning methods that acquire diverse skills without any reward function, and then repurpose these skills for downstream tasks?
Dynamics-Aware Unsupervised Discovery of Skills
Conventionally, model-based reinforcement learning (MBRL) aims to learn a global model for the dynamics of the environment.
TrueChain: Highly Performant Decentralized Public Ledger
In this paper we present the initial design of truechain consensus protocol and other technical details.
Distributed, Parallel, and Cluster Computing
