Reinforcement Learning in Factored MDPs

Multi-objective Reinforcement Learning in Factored MDPs with ...

Abstract. Many potential applications of reinforcement learning involve complex, structured environments. Some of these problems can be analyzed ...

[PDF] Efficient Reinforcement Learning in Factored MDPs

This work presents a provably efficient and near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model can ...

(PDF) Automatic Feature Selection for Model-Based Reinforcement ...

performance and reduce the computational expense of planning. Keywords-Reinforcement learning; feature selection; factored. MDPs.

TeXDYNA: Hierarchical Reinforcement Learning in Factored MDPs

Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in ...

[PDF] Near-optimal Reinforcement Learning in Factored MDPs

It is established that, if the system is known to be a factored MDP, it is possible to achieve regret that scales polynomially in the number of parameters ...

Near-optimal Regret Bounds for Reinforcement Learning in ...

However, all of these algorithms assume that the factored structure of the MDP is known to the learner in advance. ... ... Factored MDPs inherit the above ...

Optimistic Initialization and Greediness Lead to Polynomial Time ...

Efficient reinforce- ment learning in factored MDPs. International Joint. Conference on Artificial Intelligence (pp. 740–747). Kearns, M. J., & Singh, S ...

Efficient Reinforcement Learning in Factored MDPs - CiteSeerX

In particular, it cannot be ap- plied to MDPs in which the transition probabilities are rep- resented in the factored form of a dynamic Bayesian network. (DBN).

Structure Learning in Ergodic Factored MDPs without Knowledge of ...

This paper introduces Learn Structure and Exploit RMax (LSE-RMax), a novel model based structure learning algorithm for ergodic factored-state MDPs.

What is the correct interpretation of the discount factor in MDPs?

0 · You must log in to answer this question. · Browse other questions tagged. reinforcement-learning · markov-decision-process · policy-gradients ...

Factored Reinforcement Learning for Auto-scaling in Tandem Queues

Although. MDP/RL methods hold great promise for learning adaptive control policies, they can still suffer from slow convergence caused by 'curse ...

Polynomial Time Reinforcement Learning in Factored State MDPs ...

Many reinforcement learning (RL) environments in practice feature enormous state spaces that may be described compactly by a "factored" structure, ...

Random State change in MDP : r/reinforcementlearning - Reddit

It would be undeterministic behavior of environment if you'll see the mathematical equation of MDP discounting factor was added due to the ...

Efficient Reinforcement Learning in Factored MDPs with Application ...

Reinforcement learning (RL) in episodic, factored Markov decision processes (FMDPs) is studied. We propose an algorithm called FMDP-BF, which ...

TeXDYNA: Hierarchical Reinforcement Learning in Factored MDPs *

Abstract. Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational.

Learning the Structure of Factored Markov Decision Processes in ...

Algorithm-Directed Exploration for Model-Based Rein- forcement Learning in Factored MDPs. ICML-2002 The. Nineteenth International Conference on Machine Learn-.

Efficient Solution Algorithms for Factored MDPs - Stanford AI Lab

particular, we hope to address the problem of learning a factored MDP and planning in a ... International Conference on Machine Learning, Bari, Italy. Morgan ...

in factored mdps - Ian Osband

NEAR-OPTIMAL REINFORCEMENT LEARNING. IN FACTORED MDPS. IAN OSBAND AND BENJAMIN VAN ROY. STANFORD UNIVERSITY. ABSTRACT. Any reinforcement learning algorithm that ...

Feature Reinforcement Learning: Part II. Structured MDPs - Sciendo

The Feature Markov Decision Processes ( MDPs) model developed in Part I (Hutter, 2009b) is well-suited for learning agents in general ...

Sample Efficient Learning with Feature Selection for Factored MDPs

In reinforcement learning, state is often represented by feature vectors. Prior sample complexity bounds scale with the complexity of all features.