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Found 11 papers, 2 papers with code
Neural Network Training on In-memory-computing Hardware with Radix-4 Gradients
Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs).
Proper Value Equivalence
The value-equivalence (VE) principle proposes a simple answer to this question: a model should capture the aspects of the environment that are relevant for value-based planning.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Warping of Radar Data into Camera Image for Cross-Modal Supervision in Automotive Applications
As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors.
The Value Equivalence Principle for Model-Based Reinforcement Learning
As our main contribution, we introduce the principle of value equivalence: two models are value equivalent with respect to a set of functions and policies if they yield the same Bellman updates.
Model-based Reinforcement Learning
reinforcement-learning
+2
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
This is in contrast to the state-of-the-art reinforcement learning agents, which typically start learning each new task from scratch and struggle with knowledge transfer.
Learning Independently-Obtainable Reward Functions
We present a novel method for learning a set of disentangled reward functions that sum to the original environment reward and are constrained to be independently obtainable.
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model.
Learning Approximate Stochastic Transition Models
We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions.
Model-based Reinforcement Learning
reinforcement-learning
+1
Deep Abstract Q-Networks
We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards.
Summable Reparameterizations of Wasserstein Critics in the One-Dimensional Setting
Generative adversarial networks (GANs) are an exciting alternative to algorithms for solving density estimation problems---using data to assess how likely samples are to be drawn from the same distribution.
Modeling Latent Attention Within Neural Networks
Deep neural networks are able to solve tasks across a variety of domains and modalities of data.
