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Found 22 papers, 5 papers with code
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement
Hence, the proposed HCPI-RL planner has the following features: i) Evolutionary automated driving with monotonic performance enhancement; ii) With the capability of handling scenarios with emergency; iii) With enhanced decision-making optimality.
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity
For the SFT of Llama-3-8B models, GEM outperforms CE in several aspects.
ShennongAlpha: an AI-driven sharing and collaboration platform for intelligent curation, acquisition, and translation of natural medicinal material knowledge
Natural Medicinal Materials (NMMs) have a long history of global clinical applications and a wealth of records and knowledge.
Policy Optimization in RLHF: The Impact of Out-of-preference Data
These methods, either explicitly or implicitly, learn a reward model from preference data and differ in the data used for policy optimization to unlock the generalization ability of the reward model.
ReMax: A Simple, Effective, and Efficient Reinforcement Learning Method for Aligning Large Language Models
ReMax can save about 46% GPU memory than PPO when training a 7B model and enables training on A800-80GB GPUs without the memory-saving offloading technique needed by PPO.
Reward-Consistent Dynamics Models are Strongly Generalizable for Offline Reinforcement Learning
MOREC learns a generalizable dynamics reward function from offline data, which is subsequently employed as a transition filter in any offline MBRL method: when generating transitions, the dynamics model generates a batch of transitions and selects the one with the highest dynamics reward value.
Provably Efficient Adversarial Imitation Learning with Unknown Transitions
Adversarial imitation learning (AIL), a subset of IL methods, is particularly promising, but its theoretical foundation in the presence of unknown transitions has yet to be fully developed.
Theoretical Analysis of Offline Imitation With Supplementary Dataset
This paper considers a situation where, besides the small amount of expert data, a supplementary dataset is available, which can be collected cheaply from sub-optimal policies.
Understanding Adversarial Imitation Learning in Small Sample Regime: A Stage-coupled Analysis
Imitation learning learns a policy from expert trajectories.
A Survey on Model-based Reinforcement Learning
In this survey, we take a review of MBRL with a focus on the recent progress in deep RL.
Model Generation with Provable Coverability for Offline Reinforcement Learning
Model-based offline optimization with dynamics-aware policy provides a new perspective for policy learning and out-of-distribution generalization, where the learned policy could adapt to different dynamics enumerated at the training stage.
A Note on Target Q-learning For Solving Finite MDPs with A Generative Oracle
In particular, we demonstrate that the sample complexity of the target Q-learning algorithm in [Lee and He, 2020] is $\widetilde{\mathcal O}(|\mathcal S|^2|\mathcal A|^2 (1-\gamma)^{-5}\varepsilon^{-2})$.
Rethinking ValueDice: Does It Really Improve Performance?
First, we show that ValueDice could reduce to BC under the offline setting.
On Generalization of Adversarial Imitation Learning and Beyond
For some MDPs, we show that vanilla AIL has a worse sample complexity than BC.
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization
To tackle the issue of limited action execution in RL, this paper first formalizes the problem as a Sparse Action Markov Decision Process (SA-MDP), in which specific actions in the action space can only be executed for a limited time.
Generating Multi-scale Maps from Remote Sensing Images via Series Generative Adversarial Networks
The series strategy avoids RS-m inconsistency as inputs are high-resolution large-scale RSIs, and reduces the distribution gap in multi-scale map generation through similar pixel distributions among multi-scale maps.
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
As the COVID-19 pandemic continues to ravage the world, it is of critical significance to provide a timely risk prediction of the COVID-19 in multi-level.
Error Bounds of Imitating Policies and Environments
In this paper, we firstly analyze the value gap between the expert policy and imitated policies by two imitation methods, behavioral cloning and generative adversarial imitation.
Investigating Bias and Fairness in Facial Expression Recognition
Recognition of expressions of emotions and affect from facial images is a well-studied research problem in the fields of affective computing and computer vision with a large number of datasets available containing facial images and corresponding expression labels.
On Value Discrepancy of Imitation Learning
We also show that the framework leads to the value discrepancy of GAIL in an order of O((1-\gamma)^{-1}).
Deeper Interpretability of Deep Networks
However, understanding the information represented and processed in CNNs remains in most cases challenging.
A Robust Alternating Direction Method for Constrained Hybrid Variational Deblurring Model
In this work, a new constrained hybrid variational deblurring model is developed by combining the non-convex first- and second-order total variation regularizers.
