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Found 7 papers, 4 papers with code
Model-Based Reinforcement Learning with Isolated Imaginations
On top of our previous work, we further consider the sparse dependencies between controllable and noncontrollable states, address the training collapse problem of state decoupling, and validate our approach in transfer learning setups.
Predictive Experience Replay for Continual Visual Control and Forecasting
In this paper, we present a new continual learning approach for visual dynamics modeling and explore its efficacy in visual control and forecasting.
An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context
One of the key challenges in deploying RL to real-world applications is to adapt to variations of unknown environment contexts, such as changing terrains in robotic tasks and fluctuated bandwidth in congestion control.
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
This paper summarizes the common mechanism shared by twelve previous transferability-boosting methods in a unified view, i. e., these methods all reduce game-theoretic interactions between regional adversarial perturbations.
Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models
First, by optimizing the inverse dynamics, we encourage the world model to learn controllable and noncontrollable sources of spatiotemporal changes on isolated state transition branches.
Towards A Unified Understanding and Improving of Adversarial Transferability
We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations.
A Unified Approach to Interpreting and Boosting Adversarial Transferability
We discover and prove the negative correlation between the adversarial transferability and the interaction inside adversarial perturbations.
