We propose two Thompson Sampling-like, model-based learning algorithms for episodic Markov decision processes (MDPs) with a finite time horizon.
We propose two Thompson Sampling-like, model- based learning algorithms for episodic Markov de- cision processes (MDPs) with a finite time hori-.
Optimistic Thompson Sampling-Based Algorithms for Episodic Reinforcement Learning. •Real-world environments are complex and uncertain. •Training data is ...
Jul 31, 2023 · Abstract. We propose two Thompson Sampling-like, modelbased learning algorithms for episodic Markov decision processes (MDPs) with a finite time ...
People also ask
What is Thompson sampling in reinforcement learning?
What is the best algorithm for reinforcement learning?
What is the sampling theory of Thompson?
What are the main reinforcement learning algorithms?
Optimistic Thompson sampling-based algorithms for episodic reinforcement learning ... sampling: strategic exploration in bandits and reinforcement learning.
Oct 7, 2024 · Thompson sampling is a provably efficient exploration algorithm in RL (Thompson, 1933) . This approach implicitly balances exploration and ...
We present an algorithm based on posterior sampling (aka Thompson sampling) that achieves near-optimal worst-case regret bounds when the underlying Markov.
In this work, we revisit the classical bandit algorithms: upper confidence bound (UCB) and Thompson sampling (TS). We also provide a novel theoretical analysis ...
Oct 7, 2024 · Our primary contribution is the first practical model-based RL algorithm, called. Hallucination-based Optimistic Thompson sampling with Gaussian ...
Model-based reinforcement learning algorithms with probabilistic dynamical models are amongst the most data-efficient learning methods. This is often.