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Found 7 papers, 4 papers with code
Pearl: A Production-ready Reinforcement Learning Agent
Reinforcement Learning (RL) offers a versatile framework for achieving long-term goals.
Offline Reinforcement Learning for Optimizing Production Bidding Policies
We use a hybrid agent architecture that combines arbitrary base policies with deep neural networks, where only the optimized base policy parameters are eventually deployed, and the neural network part is discarded after training.
Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning
Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on user behavior.
Autoregressive Policies for Continuous Control Deep Reinforcement Learning
We introduce a family of stationary autoregressive (AR) stochastic processes to facilitate exploration in continuous control domains.
Benchmarking Reinforcement Learning Algorithms on Real-World Robots
The research community is now able to reproduce, analyze and build quickly on these results due to open source implementations of learning algorithms and simulated benchmark tasks.
Setting up a Reinforcement Learning Task with a Real-World Robot
Reinforcement learning is a promising approach to developing hard-to-engineer adaptive solutions for complex and diverse robotic tasks.
Benchmarking Quantum Hardware for Training of Fully Visible Boltzmann Machines
We argue that this relates to the fact that we are training a quantum rather than classical Boltzmann distribution in this case.
