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Found 7 papers, 5 papers with code
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs
In this work, we show how visual trajectories can be hallucinated to appear successful by altering agent observations using a generative model trained on relatively few snapshots of the goal.
Multiagent Cooperation and Competition with Deep Reinforcement Learning
In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.
Combining Static and Dynamic Features for Multivariate Sequence Classification
In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data.
Understanding Information Processing in Human Brain by Interpreting Machine Learning Models
Combined with interpretability techniques, machine learning could replace human modeler and shift the focus of human effort to extracting the knowledge from the ready-made models and articulating that knowledge into intuitive descroptions of reality.
Direct information transfer rate optimisation for SSVEP-based BCI
The proposed method shows good performance in classifying targets of a BCI, outperforming previously reported results on the same dataset by a factor of 2 in terms of ITR.
OffWorld Gym: open-access physical robotics environment for real-world reinforcement learning benchmark and research
The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves.
Offline Robot Reinforcement Learning with Uncertainty-Guided Human Expert Sampling
Although offline learning techniques can learn from data generated by a sub-optimal behavior agent, there is still an opportunity to improve the sample complexity of existing offline reinforcement learning algorithms by strategically introducing human demonstration data into the training process.
