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Found 25 papers, 11 papers with code
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
In this paper, we propose RLExplorer, the first fault diagnosis approach for DRL-based software systems.
Mitigating Unsafe Feedback with Learning Constraints
While there has been progress towards aligning Large Language Models (LLMs) with human values and ensuring safe behaviour at inference time, safety-guards can easily be removed when fine-tuned on unsafe and harmful datasets. While this setting has been treated extensively, another popular training paradigm, learning from unsafe feedback with reinforcement learning, has previously been unexplored.
Intelligent Switching for Reset-Free RL
In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable.
Fairness Incentives in Response to Unfair Dynamic Pricing
We find that, upon deploying a learned tax and redistribution policy, social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings, and surpassing it by 13. 19% in the full RL setting.
Mastering Memory Tasks with World Models
Through a diverse set of illustrative tasks, we systematically demonstrate that R2I not only establishes a new state-of-the-art for challenging memory and credit assignment RL tasks, such as BSuite and POPGym, but also showcases superhuman performance in the complex memory domain of Memory Maze.
Language Model-In-The-Loop: Data Optimal Approach to Learn-To-Recommend Actions in Text Games
In this work, we explore and evaluate updating LLM used for candidate recommendation during the learning of the text based game as well to mitigate the reliance on the human annotated gameplays, which are costly to acquire.
Towards Few-shot Coordination: Revisiting Ad-hoc Teamplay Challenge In the Game of Hanabi
In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners.
Conditionally Optimistic Exploration for Cooperative Deep Multi-Agent Reinforcement Learning
Efficient exploration is critical in cooperative deep Multi-Agent Reinforcement Learning (MARL).
Replay Buffer with Local Forgetting for Adapting to Local Environment Changes in Deep Model-Based Reinforcement Learning
This is challenging for deep-learning-based world models due to catastrophic forgetting.
Model-based Reinforcement Learning
reinforcement-learning
+1
Dealing With Non-stationarity in Decentralized Cooperative Multi-Agent Deep Reinforcement Learning via Multi-Timescale Learning
In our proposed method, when one agent updates its policy, other agents are allowed to update their policies as well, but at a slower rate.
PatchBlender: A Motion Prior for Video Transformers
We show that our method is successful at enabling vision transformers to encode the temporal component of video data.
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods
We empirically validate these insights in the case of linear function approximation by demonstrating that a modified version of linear Dyna achieves effective adaptation to local changes.
Model-based Reinforcement Learning
reinforcement-learning
+2
Learning to Learn End-to-End Goal-Oriented Dialog From Related Dialog Tasks
For each goal-oriented dialog task of interest, large amounts of data need to be collected for end-to-end learning of a neural dialog system.
Reinforcement Learning of Implicit and Explicit Control Flow in Instructions
Learning to flexibly follow task instructions in dynamic environments poses interesting challenges for reinforcement learning agents.
Quantifying the Effects of COVID-19 on Mental Health Support Forums
The COVID-19 pandemic, like many of the disease outbreaks that have preceded it, is likely to have a profound effect on mental health.
Meta-Learning Requires Meta-Augmentation
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new task.
How Should an Agent Practice?
We present a method for learning intrinsic reward functions to drive the learning of an agent during periods of practice in which extrinsic task rewards are not available.
Discovery of Useful Questions as Auxiliary Tasks
Arguably, intelligent agents ought to be able to discover their own questions so that in learning answers for them they learn unanticipated useful knowledge and skills; this departs from the focus in much of machine learning on agents learning answers to externally defined questions.
Learning End-to-End Goal-Oriented Dialog with Maximal User Task Success and Minimal Human Agent Use
In this work, we propose an end-to-end trainable method for neural goal-oriented dialog systems which handles new user behaviors at deployment by transferring the dialog to a human agent intelligently.
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting.
NE-Table: A Neural key-value table for Named Entities
Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources.
A Neural Method for Goal-Oriented Dialog Systems to interact with Named Entities
Many goal-oriented dialog tasks, especially ones in which the dialog system has to interact with external knowledge sources such as databases, have to handle a large number of Named Entities (NEs).
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications).
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
In this work, we address a real-world scenario where no direct parallel data is available between two views of interest (say, $V_1$ and $V_2$) but parallel data is available between each of these views and a pivot view ($V_3$).
Attend, Adapt and Transfer: Attentive Deep Architecture for Adaptive Transfer from multiple sources in the same domain
Second, the agent should be able to selectively transfer, which is the ability to select and transfer from different and multiple source tasks for different parts of the state space of the target task.
