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Found 14 papers, 9 papers with code
From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood
Our goal is to learn a semantic parser that maps natural language utterances into executable programs when only indirect supervision is available: examples are labeled with the correct execution result, but not the program itself.
Reinforcement Learning on Web Interfaces Using Workflow-Guided Exploration
Reinforcement learning (RL) agents improve through trial-and-error, but when reward is sparse and the agent cannot discover successful action sequences, learning stagnates.
Mapping Natural Language Commands to Web Elements
The web provides a rich, open-domain environment with textual, structural, and spatial properties.
Learning Abstract Models for Long-Horizon Exploration
In our approach, a manager maintains an abstract MDP over a subset of the abstract states, which grows monotonically through targeted exploration (possible due to the abstract MDP).
Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning
In principle, meta-reinforcement learning approaches can exploit this shared structure, but in practice, they fail to adapt to new environments when adaptation requires targeted exploration (e. g., exploring the cabinets to find ingredients in a new kitchen).
An Imitation Learning Approach for Cache Replacement
While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot.
Learning Abstract Models for Strategic Exploration and Fast Reward Transfer
Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.
Just Train Twice: Improving Group Robustness without Training Group Information
Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
Analyzing a Caching Model
Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics.
Giving Feedback on Interactive Student Programs with Meta-Exploration
However, teaching and giving feedback on such software is time-consuming -- standard approaches require instructors to manually grade student-implemented interactive programs.
Learning Options via Compression
Identifying statistical regularities in solutions to some tasks in multi-task reinforcement learning can accelerate the learning of new tasks.
A Survey of Meta-Reinforcement Learning
Meta-RL is most commonly studied in a problem setting where, given a distribution of tasks, the goal is to learn a policy that is capable of adapting to any new task from the task distribution with as little data as possible.
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
Specifically, we design an office navigation environment, where the agent's goal is to find a particular office, and office locations differ in different buildings (i. e., tasks).
