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Found 8 papers, 2 papers with code
Safety-aware Causal Representation for Trustworthy Offline Reinforcement Learning in Autonomous Driving
However, maintaining safety in diverse safety-critical scenarios remains a significant challenge due to long-tailed and unforeseen scenarios absent from offline datasets.
Datasets and Benchmarks for Offline Safe Reinforcement Learning
This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the training and deployment phases.
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning
As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations.
Rethinking Controllable Variational Autoencoders
It has shown success in a variety of applications, such as image generation, disentangled representation learning, and language modeling.
CausalAF: Causal Autoregressive Flow for Safety-Critical Driving Scenario Generation
Generating safety-critical scenarios, which are crucial yet difficult to collect, provides an effective way to evaluate the robustness of autonomous driving systems.
Semantically Adversarial Scenario Generation with Explicit Knowledge Guidance
Generating adversarial scenarios, which have the potential to fail autonomous driving systems, provides an effective way to improve robustness.
Controllable and Diverse Text Generation in E-commerce
The algorithm is injected into a Conditional Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the order of keywords in the generated sentences (conditioned on the input keywords and their order), and (ii) the trade-off between diversity and accuracy.
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning
Existing methods, such as $\beta$-VAE and FactorVAE, assign a large weight to the KL-divergence term in the objective function, leading to high reconstruction errors for the sake of better disentanglement.
