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Found 8 papers, 5 papers with code
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory
Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator.
Noise Contrastive Alignment of Language Models with Explicit Rewards
Existing alignment methods, such as Direct Preference Optimization (DPO), are mainly tailored for pairwise preference data where rewards are implicitly defined rather than explicitly given.
Score Regularized Policy Optimization through Diffusion Behavior
Recent developments in offline reinforcement learning have uncovered the immense potential of diffusion modeling, which excels at representing heterogeneous behavior policies.
Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to estimate.
Offline Reinforcement Learning via High-Fidelity Generative Behavior Modeling
To address this problem, we adopt a generative approach by decoupling the learned policy into two parts: an expressive generative behavior model and an action evaluation model.
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition
Cross-dataset emotion recognition as an extremely challenging task in the field of EEG-based affective computing is influenced by many factors, which makes the universal models yield unsatisfactory results.
Tianshou: a Highly Modularized Deep Reinforcement Learning Library
In this paper, we present Tianshou, a highly modularized Python library for deep reinforcement learning (DRL) that uses PyTorch as its backend.
A study of resting-state EEG biomarkers for depression recognition
Results: Functional connectivity feature PLI is superior to the linear features and nonlinear features.
