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Found 11 papers, 8 papers with code
Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers
Non-linear functions are prevalent in Transformers and their lightweight variants, incurring substantial and frequently underestimated hardware costs.
Do Transformer World Models Give Better Policy Gradients?
We integrate such AWMs into a policy gradient framework that underscores the relationship between network architectures and the policy gradient updates they inherently represent.
Bridging State and History Representations: Understanding Self-Predictive RL
These findings culminate in a set of preliminary guidelines for RL practitioners.
When Do Transformers Shine in RL? Decoupling Memory from Credit Assignment
The Transformer architecture has been very successful to solve problems that involve long-term dependencies, including in the RL domain.
Towards Disturbance-Free Visual Mobile Manipulation
In this paper, we study the problem of training agents to complete the task of visual mobile manipulation in the ManipulaTHOR environment while avoiding unnecessary collision (disturbance) with objects.
Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs
However, prior work has found that such recurrent model-free RL methods tend to perform worse than more specialized algorithms that are designed for specific types of POMDPs.
Adaptive Agent Architecture for Real-time Human-Agent Teaming
Previous human-human team research have shown complementary policies in TSF game and diversity in human players' skill, which encourages us to relax the assumptions on human policy.
f-IRL: Inverse Reinforcement Learning via State Marginal Matching
Our method outperforms adversarial imitation learning methods in terms of sample efficiency and the required number of expert trajectories on IRL benchmarks.
Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
Our method is built upon the Soft Actor-Critic (SAC) algorithm, which uses an "entropy temperature" that balances the original task reward and the policy entropy, and hence controls the trade-off between exploitation and exploration.
Elastic Boundary Projection for 3D Medical Image Segmentation
The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.
Phase Collaborative Network for Two-Phase Medical Image Segmentation
However, in medical image analysis, fusing prediction from two phases is often difficult, because (i) there is a domain gap between two phases, and (ii) the semantic labels are not pixel-wise corresponded even for images scanned from the same patient.
