Incremental Learning of Discrete Planning Domains from Continuous Perceptions

03/14/2019
by   Luciano Serafini, et al.
0

We propose a framework for learning discrete deterministic planning domains. In this framework, an agent learns the domain by observing the action effects through continuous features that describe the state of the environment after the execution of each action. Besides, the agent learns its perception function, i.e., a probabilistic mapping between state variables and sensor data represented as a vector of continuous random variables called perception variables. We define an algorithm that updates the planning domain and the perception function by (i) introducing new states, either by extending the possible values of state variables, or by weakening their constraints; (ii) adapts the perception function to fit the observed data (iii) adapts the transition function on the basis of the executed actions and the effects observed via the perception function. The framework is able to deal with exogenous events that happen in the environment.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/16/2018

Learning abstract planning domains and mappings to real world perceptions

Most of the works on planning and learning, e.g., planning by (model bas...
research
10/19/2021

Gradient-Based Mixed Planning with Discrete and Continuous Actions

Dealing with planning problems with both discrete logical relations and ...
research
06/08/2021

Vector Quantized Models for Planning

Recent developments in the field of model-based RL have proven successfu...
research
02/27/2013

A Structured, Probabilistic Representation of Action

When agents devise plans for execution in the real world, they face two ...
research
01/15/2014

A Heuristic Search Approach to Planning with Continuous Resources in Stochastic Domains

We consider the problem of optimal planning in stochastic domains with r...
research
05/22/2019

Minimizing the Negative Side Effects of Planning with Reduced Models

Reduced models of large Markov decision processes accelerate planning by...
research
10/03/2020

Episodic Memory for Learning Subjective-Timescale Models

In model-based learning, an agent's model is commonly defined over trans...

Please sign up or login with your details

Forgot password? Click here to reset