Reinforcement Learning
Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model
The curse of dimensionality is a widely known issue in reinforcement learning (RL).
Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm
During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps.
A Self-Tuning Actor-Critic Algorithm
In this paper, we take a step towards addressing this issue by using metagradients to automatically adapt hyperparameters online by meta-gradient descent (Xu et al., 2018).
Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses Haipeng Luo Chen-Y u Wei
Policy optimization is a widely-used method in reinforcement learning.
Learning in Non-Cooperative Configurable Markov Decision Processes Giorgia Ramponi ETH AI Center Zurich, Switzerland gramponi@ethz.ch Alberto Maria Metelli Politecnico di Milano Milan, Italy
Reinforcement Learning agent and a configurator that can modify some environmental parameters to improve the agent's performance.
Counterexample Guided RL Policy Refinement Using Bayesian Optimization
Constructing Reinforcement Learning (RL) policies that adhere to safety requirements is an emerging field of study.