29 research outputs found
Inverse Reinforcement Learning in Swarm Systems
Inverse reinforcement learning (IRL) has become a useful tool for learning
behavioral models from demonstration data. However, IRL remains mostly
unexplored for multi-agent systems. In this paper, we show how the principle of
IRL can be extended to homogeneous large-scale problems, inspired by the
collective swarming behavior of natural systems. In particular, we make the
following contributions to the field: 1) We introduce the swarMDP framework, a
sub-class of decentralized partially observable Markov decision processes
endowed with a swarm characterization. 2) Exploiting the inherent homogeneity
of this framework, we reduce the resulting multi-agent IRL problem to a
single-agent one by proving that the agent-specific value functions in this
model coincide. 3) To solve the corresponding control problem, we propose a
novel heterogeneous learning scheme that is particularly tailored to the swarm
setting. Results on two example systems demonstrate that our framework is able
to produce meaningful local reward models from which we can replicate the
observed global system dynamics.Comment: 9 pages, 8 figures; ### Version 2 ### version accepted at AAMAS 201
Efficient Probabilistic Performance Bounds for Inverse Reinforcement Learning
In the field of reinforcement learning there has been recent progress towards
safety and high-confidence bounds on policy performance. However, to our
knowledge, no practical methods exist for determining high-confidence policy
performance bounds in the inverse reinforcement learning setting---where the
true reward function is unknown and only samples of expert behavior are given.
We propose a sampling method based on Bayesian inverse reinforcement learning
that uses demonstrations to determine practical high-confidence upper bounds on
the -worst-case difference in expected return between any evaluation
policy and the optimal policy under the expert's unknown reward function. We
evaluate our proposed bound on both a standard grid navigation task and a
simulated driving task and achieve tighter and more accurate bounds than a
feature count-based baseline. We also give examples of how our proposed bound
can be utilized to perform risk-aware policy selection and risk-aware policy
improvement. Because our proposed bound requires several orders of magnitude
fewer demonstrations than existing high-confidence bounds, it is the first
practical method that allows agents that learn from demonstration to express
confidence in the quality of their learned policy.Comment: In proceedings AAAI-1
ICRA Roboethics Challenge 2023: Intelligent Disobedience in an Elderly Care Home
With the projected surge in the elderly population, service robots offer a
promising avenue to enhance their well-being in elderly care homes. Such robots
will encounter complex scenarios which will require them to perform decisions
with ethical consequences. In this report, we propose to leverage the
Intelligent Disobedience framework in order to give the robot the ability to
perform a deliberation process over decisions with potential ethical
implications. We list the issues that this framework can assist with, define it
formally in the context of the specific elderly care home scenario, and
delineate the requirements for implementing an intelligently disobeying robot.
We conclude this report with some critical analysis and suggestions for future
work.Comment: This report is part of ICRA roboethics competition :
https://competition.raiselab.ca/competition-details-2023_1/ethics-challenge/submitted-proposals/submission-
Inverse Reinforcement Learning through Policy Gradient Minimization
Inverse Reinforcement Learning (IRL) deals with the problem of recovering the reward function optimized by an expert given a set of demonstrations of the expert's policy.Most IRL algorithms need to repeatedly compute the optimal policy for different reward functions.This paper proposes a new IRL approach that allows to recover the reward function without the need of solving any "direct" RL problem.The idea is to find the reward function that minimizes the gradient of a parameterized representation of the expert's policy.In particular, when the reward function can be represented as a linear combination of some basis functions, we will show that the aforementioned optimization problem can be efficiently solved.We present an empirical evaluation of the proposed approach on a multidimensional version of the Linear-Quadratic Regulator (LQR) both in the case where the parameters of the expert's policy are known and in the (more realistic) case where the parameters of the expert's policy need to be inferred from the expert's demonstrations.Finally, the algorithm is compared against the state-of-the-art on the mountain car domain, where the expert's policy is unknown
A comparative study between motivated learning and reinforcement learning
This paper analyzes advanced reinforcement learning techniques and compares some of them to motivated learning. Motivated learning is briefly discussed indicating its relation to reinforcement learning. A black box scenario for comparative analysis of learning efficiency in autonomous agents is developed and described. This is used to analyze selected algorithms. Reported results demonstrate that in the selected category of problems, motivated learning outperformed all reinforcement learning algorithms we compared with