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Found 12 papers, 7 papers with code
Premier-TACO is a Few-Shot Policy Learner: Pretraining Multitask Representation via Temporal Action-Driven Contrastive Loss
We present Premier-TACO, a multitask feature representation learning approach designed to improve few-shot policy learning efficiency in sequential decision-making tasks.
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications.
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences
However, current MLLM benchmarks are predominantly designed to evaluate reasoning based on static information about a single image, and the ability of modern MLLMs to extrapolate from image sequences, which is essential for understanding our ever-changing world, has been less investigated.
DrM: Mastering Visual Reinforcement Learning through Dormant Ratio Minimization
To quantify this inactivity, we adopt dormant ratio as a metric to measure inactivity in the RL agent's network.
COPlanner: Plan to Roll Out Conservatively but to Explore Optimistically for Model-Based RL
In this paper, we propose $\texttt{COPlanner}$, a planning-driven framework for model-based methods to address the inaccurately learned dynamics model problem with conservative model rollouts and optimistic environment exploration.
Equal Long-term Benefit Rate: Adapting Static Fairness Notions to Sequential Decision Making
Moreover, we show that the policy gradient of Long-term Benefit Rate can be analytically reduced to standard policy gradient.
TACO: Temporal Latent Action-Driven Contrastive Loss for Visual Reinforcement Learning
Despite recent progress in reinforcement learning (RL) from raw pixel data, sample inefficiency continues to present a substantial obstacle.
Is Model Ensemble Necessary? Model-based RL via a Single Model with Lipschitz Regularized Value Function
To test this hypothesis, we devise two practical robust training mechanisms through computing the adversarial noise and regularizing the value network's spectral norm to directly regularize the Lipschitz condition of the value functions.
Live in the Moment: Learning Dynamics Model Adapted to Evolving Policy
Model-based reinforcement learning (RL) often achieves higher sample efficiency in practice than model-free RL by learning a dynamics model to generate samples for policy learning.
Transfer RL across Observation Feature Spaces via Model-Based Regularization
In many reinforcement learning (RL) applications, the observation space is specified by human developers and restricted by physical realizations, and may thus be subject to dramatic changes over time (e. g. increased number of observable features).
A Two-branch Neural Network for Non-homogeneous Dehazing via Ensemble Learning
Recently, there has been rapid and significant progress on image dehazing.
Planning with Exploration: Addressing Dynamics Bottleneck in Model-based Reinforcement Learning
We give an upper bound of the trajectory reward estimation error and point out that increasing the agent's exploration ability is the key to reduce trajectory reward estimation error, thereby alleviating dynamics bottleneck dilemma.
