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Classic meets modern: A pragmatic learning-based congestion control for the internet
These days, taking the revolutionary approach of using clean-slate learning-based designs
to completely replace the classic congestion control schemes for the Internet is gaining …
to completely replace the classic congestion control schemes for the Internet is gaining …
Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
We present a coarse-to-fine discretisation method that enables the use of discrete
reinforcement learning approaches in place of unstable and data-inefficient actor-critic …
reinforcement learning approaches in place of unstable and data-inefficient actor-critic …
Auto: Scaling deep reinforcement learning for datacenter-scale automatic traffic optimization
Traffic optimizations (TO, eg flow scheduling, load balancing) in datacenters are difficult
online decision-making problems. Previously, they are done with heuristics relying on …
online decision-making problems. Previously, they are done with heuristics relying on …
On the effectiveness of fine-tuning versus meta-reinforcement learning
Intelligent agents should have the ability to leverage knowledge from previously learned
tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have …
tasks in order to learn new ones quickly and efficiently. Meta-learning approaches have …
Learning basketball dribbling skills using trajectory optimization and deep reinforcement learning
Basketball is one of the world's most popular sports because of the agility and speed
demonstrated by the players. This agility and speed makes designing controllers to realize …
demonstrated by the players. This agility and speed makes designing controllers to realize …
Learning to schedule control fragments for physics-based characters using deep q-learning
Given a robust control system, physical simulation offers the potential for interactive human
characters that move in realistic and responsive ways. In this article, we describe how to …
characters that move in realistic and responsive ways. In this article, we describe how to …
Sustaingym: Reinforcement learning environments for sustainable energy systems
The lack of standardized benchmarks for reinforcement learning (RL) in sustainability
applications has made it difficult to both track progress on specific domains and identify …
applications has made it difficult to both track progress on specific domains and identify …
Physics-based motion capture imitation with deep reinforcement learning
We introduce a deep reinforcement learning method that learns to control articulated
humanoid bodies to imitate given target motions closely when simulated in a physics …
humanoid bodies to imitate given target motions closely when simulated in a physics …
Urban driving with multi-objective deep reinforcement learning
C Li, K Czarnecki - arxiv preprint arxiv:1811.08586, 2018 - arxiv.org
Autonomous driving is a challenging domain that entails multiple aspects: a vehicle should
be able to drive to its destination as fast as possible while avoiding collision, obeying traffic …
be able to drive to its destination as fast as possible while avoiding collision, obeying traffic …
Learning hierarchical teaching policies for cooperative agents
Collective learning can be greatly enhanced when agents effectively exchange knowledge
with their peers. In particular, recent work studying agents that learn to teach other …
with their peers. In particular, recent work studying agents that learn to teach other …