Using Semantic Similarity as Reward for Reinforcement Learning in ...

Language-Informed Transfer Learning for Embodied Household ...

Semantic Similarity Given a new activity with an initial state and a set of ... Transfer. Learning in Deep Reinforcement Learning: A Survey. arXiv ...

Unsupervised Paraphrasing via Deep Reinforcement Learning

Semantic Adequacy: The semantic adequacy reward rSim (X, ˆY) ... Then, the semantic similarity can be calculated using cosine simi- larity of ...

Enhancing Generative Retrieval with Reinforcement Learning from ...

Semantic Similarity. This goes beyond term over- lap to look at the meaning ... Figure 4(b) shows the learning curve with different scale of the reward model.

Reviewing Evolution of Learning Functions and Semantic ... - MDPI

It also discusses how the SeMI measure is used as the reward function (reflecting purposiveness) for reinforcement learning. The G theory helps interpret deep ...

Pushing RL Boundaries: Integrating Foundational Models, e.g.

The heart of such algorithms is the use of a preference dataset to train a reward model which can subsequently be integrated into reinforcement ...

Goal-Induced Inverse Reinforcement Learning - UC Berkeley EECS

framework for learning a reward function for the Reinforcement Learning problem with ... semantic similarity of the corre- sponding words.

Transfer Learning for Reinforcement Learning Domains: A Survey

... similarity depends on the transfer and reward functions in the two MDPs. ... Specifically, Lazaric (2008) learns a set of tasks with different reward functions ...

Using Deep Reinforcement Learning for the Adaptation of Semantic ...

The domain-independent rewards feature the semantic similarity (see Sect. 2.1) between the query workflow and the case workflow [10]. If the ...

DRL4NLP: - UCSB Computer Science

Language understanding for text- based games using deep reinforcement learning. ... reward how a sentence fits with surrounding sentences. 3. A DSSM like ...

Journal Papers - Kemal Oflazer - Google Sites

27) Figen Fikri Beken, Kemal Oflazer, Berrin Yanıkoǧlu, Abstractive Summarization with Deep Reinforcement Learning using Semantic Similarity Rewards, ...

Chenguang Lu, Reviewing Evolution of Learning Functions and ...

It also discusses how the SeMI measure is used as the reward function (reflecting purposiveness) for reinforcement learning. The G theory helps interpret deep ...

Evaluating BERT-based Rewards for Question Generation with ...

Prior works have identi- fied a range of shortcomings (including semantic drift and exposure bias) and thus have turned to the reinforcement learning paradigm.

Reward Design for Deep Reinforcement Learning Towards ...

different ESDs are combined by semantic similarity by humans. For example ... Efficient sampling-based maximum entropy inverse reinforcement learning with ...

Semantic Similarity | Papers With Code

A Deep Reinforced Model for Zero-Shot Cross-Lingual Summarization with Bilingual Semantic Similarity Rewards ... The model uses reinforcement learning to directly ...

SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement ...

with negative cosine similarity as follows: D(p1,z2) ... Integrating con- trastive learning with dynamic models for reinforcement learning from images.

A Deep Reinforced Model for Zero-Shot Cross-Lingual - S-Logix

The model uses reinforcement learning to directly optimize a bilingual semantic similarity metric between the summaries generated in a target language and gold ...

Reinforcement Learning with Deep Structured Semantic Model

Similarity. Transition probability: ! ′| ,. Page 3. The model ... This learning procedure actually incorporates the long-term reward information and also.

Model-free Reinforcement Learning of Semantic Communication by ...

In [7], the authors suggest using semantic similarity as the objective function: As most semantic metrics are non-differentiable, they propose a self-.

Paraphrase Generation Using Deep Reinforcement Learning

The problem of generating the best paraphrase can be viewed as finding the series of words which maximizes the semantic similarity between ...

RL-VLM-F: Reinforcement Learning from Vision Language ...

Given an image, the reward is computed as the cosine similarity score between the embedding of the image and the text descrip- tion of the task goal using the ...