Near|optimal Reinforcement Learning in Factored MDPs

Probably Approximately Corrct (PAC) Exploration in Reinforcement ...

the optimal action-value function of an MDP, which we call Model(R-MAX). This ... ment learning in factored MDPs. In Proceedings of the. International ...

Markov Decision Process in Reinforcement Learning - neptune.ai

The goal of the MDP m is to find a policy, often denoted as pi, that yields the optimal long-term reward. Policies are simply a mapping of ...

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

Brafman, Ronen I. and Tennenholtz, Moshe. R-max - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learn-.

REGAL Revisited: Regularized Reinforcement Learning for Weakly ...

nicating MDPs, and in the case of multiple optimal MDPs, the MDP with the largest span ... Tewari, Reinforcement learning in factored mdps: Oracle-efficient algo-.

Near-Optimal Reinforcement Learning in Polynomial Time

definitions for MDPs and reinforcement learning. In ... This has been recently investigated in the context of factored MDPs (Kearns & Koller, 1999).

ICLR Poster Efficient Reinforcement Learning in Factored MDPs ...

Poster. Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL. Xiaoyu Chen · Jiachen Hu · Lihong Li · Liwei Wang. Virtual.

Structured Kernel-Based Reinforcement Learning

Our analy- sis reveals that structured KBRL is equivalent to computing the optimal value function in a special factored MDP. To the best of our knowledge ...

Efficient approximate linear programming for factored MDPs

Factored Markov Decision Processes (MDPs) provide a compact representation for modeling sequential decision making problems with many variables.

L1 MDPs, Exact Solution Methods, Max-ent RL (Foundations of ...

Lecture 1 of a 6-lecture series on the Foundations of Deep RL Topic: MDPs ... This guy is seriously the god of reinforcement learning. He and ...

PROBABLY APPROXIMATELY CORRECT (PAC) EXPLORATION IN ...

rithm for near-optimal reinforcement learning. Journal of Machine ... for model-based reinforcement learning in factored MDPs. Proceedings of the ...

Sample-Efficient Reinforcement Learning for Linearly ...

This paper considers a Markov decision process (MDP) that admits a set of state-action features, which can linearly express (or approximate) its probability ...

Automatic Feature Selection for Model-Based Reinforcement ...

Polynomial Time Algorithm for Near-Optimal Reinforcement Learning,” ... exploration for model-based reinforcement learning in factored MDPs,” in ...

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

Brafman, Ronen I. and Tennenholtz, Moshe. R-max - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learn-.

Factored MDPs - DAGS - Daphne Koller's Research Group

In our work on reinforcement learning in continuous-state MDPs we focus on kernel-based methods to obtain statistical accuracy while simultaneously guaranteeing ...

Policy Error Bounds for Model-Based Reinforcement Learning with ...

We also show examples of MBRL approaches that use factored linear models. In a factored linear model we approximate the MDP's stochastic kernel P as the product ...

Theoretical Foundations of Reinforcement Learning

Ian Osband and Benjamin Van Roy. Near-optimal reinforcement learning in factored MDPs. NeurIPS, 2014. Aviv Rosenberg and Yishay Mansour. Oracle-efficient ...

Safe Exploration in Markov Decision Processes - People @EECS

by finding optimal policies in constructed MDPs with exploration ... R-MAX - A. General Polynomial Time Algorithm for Near-Optimal. Reinforcement Learning.

Markov decision process - Wikipedia

Reinforcement learning utilizes the MDP framework to model the interaction between a learning agent and its environment. In this framework, the interaction is ...

Online reinforcement learning for condition-based ... - Strathprints

For the nominal model with known parameters,. Section 4 presents a modified factored value iteration algorithm to compute an optimal maintenance ...

Efficient Exploration in Reinforcement Learning - SpringerLink

For all MDPs, for any δ > 0, with probability 1 − δ, the algorithm delayed Q-learning finds an ε-optimal policy after Õ(SA) explorations. The delayed Q-learning ...