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Found 11 papers, 5 papers with code
QuadSwarm: A Modular Multi-Quadrotor Simulator for Deep Reinforcement Learning with Direct Thrust Control
In addition to speed, such simulators need to model the physics of the robots and their interaction with the environment to a level acceptable for transferring policies learned in simulation to reality.
Cognitive Architecture for Decision-Making Based on Brain Principles Programming (in Russian)
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.
Cognitive Architecture for Decision-Making Based on Brain Principles Programming
We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory.
Brain Principles Programming
In this paper, for the description and modeling of Brain Principles Programming, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories.
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation
Predicting the future motion of traffic agents is crucial for safe and efficient autonomous driving.
Generalized Inner Loop Meta-Learning
Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem.
Meta Learning via Learned Loss
We present a meta-learning method for learning parametric loss functions that can generalize across different tasks and model architectures.
Meta-Learning via Learned Loss
This information shapes the learned loss function such that the environment does not need to provide this information during meta-test time.
Sim-to-(Multi)-Real: Transfer of Low-Level Robust Control Policies to Multiple Quadrotors
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation.
Robotics
Region Growing Curriculum Generation for Reinforcement Learning
In this work, we introduce a method based on region-growing that allows learning in an environment with any pair of initial and goal states.
Synthetically Trained Neural Networks for Learning Human-Readable Plans from Real-World Demonstrations
We present a system to infer and execute a human-readable program from a real-world demonstration.
Robotics
