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Found 10 papers, 6 papers with code
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
Recent methods for imitation learning directly learn a $Q$-function using an implicit reward formulation rather than an explicit reward function.
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
The two currently most effective methods for GMM-based variational inference, VIPS and iBayes-GMM, both employ independent natural gradient updates for the individual components and their weights.
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning
Recent works based on state-visitation counts, curiosity and entropy-maximization generate intrinsic reward signals to motivate the agent to visit novel states for exploration.
Integrating Contrastive Learning with Dynamic Models for Reinforcement Learning from Images
Recent methods for reinforcement learning from images use auxiliary tasks to learn image features that are used by the agent's policy or Q-function.
A First-Order Method for Estimating Natural Gradients for Variational Inference with Gaussians and Gaussian Mixture Models
Effective approaches for Gaussian variational inference are MORE, VOGN, and VON, which are zero-order, first-order, and second-order, respectively.
Non-Adversarial Imitation Learning and its Connections to Adversarial Methods
We also show that our non-adversarial formulation can be used to derive novel algorithms by presenting a method for offline imitation learning that is inspired by the recent ValueDice algorithm, but does not rely on small policy updates for convergence.
Deep Adversarial Reinforcement Learning for Object Disentangling
The ARL framework utilizes an adversary, which is trained to steer the original agent, the protagonist, to challenging states.
Expected Information Maximization: Using the I-Projection for Mixture Density Estimation
Such behavior is appealing whenever we deal with highly multi-modal data where modelling single modes correctly is more important than covering all the modes.
Trust-Region Variational Inference with Gaussian Mixture Models
For efficient improvement of the GMM approximation, we derive a lower bound on the corresponding optimization objective enabling us to update the components independently.
Efficient Gradient-Free Variational Inference using Policy Search
Inference from complex distributions is a common problem in machine learning needed for many Bayesian methods.
