Sequential Decision Making
303 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Sequential Decision Making
Libraries
Use these libraries to find Sequential Decision Making models and implementationsMost implemented papers
Deep Reinforcement Learning for Unsupervised Video Summarization with Diversity-Representativeness Reward
KaiyangZhou/vsumm-reinforce
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Video summarization aims to facilitate large-scale video browsing by producing short, concise summaries that are diverse and representative of original videos.
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions Modeling
sb-ai-lab/RePlay
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The DRR framework treats recommendation as a sequential decision making procedure and adopts an "Actor-Critic" reinforcement learning scheme to model the interactions between the users and recommender systems, which can consider both the dynamic adaptation and long-term rewards.
IQ-Learn: Inverse soft-Q Learning for Imitation
Div99/IQ-Learn
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NeurIPS 2021
In many sequential decision-making problems (e. g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task.
Reflexion: Language Agents with Verbal Reinforcement Learning
Large language models (LLMs) have been increasingly used to interact with external environments (e. g., games, compilers, APIs) as goal-driven agents.
Thinking Fast and Slow with Deep Learning and Tree Search
richemslie/galvanise_zero
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NeurIPS 2017
Sequential decision making problems, such as structured prediction, robotic control, and game playing, require a combination of planning policies and generalisation of those plans.
Learning Multi-Level Hierarchies with Hindsight
andrew-j-levy/Hierarchical-Actor-Critc-HAC-
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Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions.
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
tensorflow/models
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ICLR 2018
At the same time, advances in approximate Bayesian methods have made posterior approximation for flexible neural network models practical.
Model-Free Episodic Control
State of the art deep reinforcement learning algorithms take many millions of interactions to attain human-level performance.
A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping
wuhuikai/TF-A2RL
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CVPR 2018
Image cropping aims at improving the aesthetic quality of images by adjusting their composition.
Deep Reinforcement Learning for Imbalanced Classification
linenus/DRL-For-imbalanced-Classification
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The agent finally finds an optimal classification policy in imbalanced data under the guidance of specific reward function and beneficial learning environment.
