Reinaldo A. C. Bianchi

Accelerating autonomous learning by using heuristic selection of actions

Abstract This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process. With this aim, we investigate the use of heuristics for increasing the rate of convergence of RL algorithms and contribute with a new learning algorithm, Heuristically Accelerated Q-learning (HAQL), which incorporates heuristics for action selection to the Q-Learning algorithm. Experimental results on robot navigation show that the use of even very simple heuristic functions results in significant performance enhancement of the learning rate.

Heuristically Accelerated Q–Learning: A New Approach to Speed Up Reinforcement Learning

This work presents a new algorithm, called Heuristically Accelerated Q–Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q–learning. A heuristic function \(\mathcal{H}\) that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it indicates that an action must be taken instead of another. This work also proposes an automatic method for the extraction of the heuristic function \(\mathcal{H}\) from the learning process, called Heuristic from Exploration. Finally, experimental results shows that even a very simple heuristic results in a significant enhancement of performance of the reinforcement learning algorithm.

Improving Reinforcement Learning by Using Case Based Heuristics

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q---Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.

Heuristically-Accelerated Multiagent Reinforcement Learning

This paper presents a novel class of algorithms, called Heuristically-Accelerated Multiagent Reinforcement Learning (HAMRL), which allows the use of heuristics to speed up well-known multiagent reinforcement learning (RL) algorithms such as the Minimax-Q. Such HAMRL algorithms are characterized by a heuristic function, which suggests the selection of particular actions over others. This function represents an initial action selection policy, which can be handcrafted, extracted from previous experience in distinct domains, or learnt from observation. To validate the proposal, a thorough theoretical analysis proving the convergence of four algorithms from the HAMRL class (HAMMQ, HAMQ(λ), HAMQS, and HAMS) is presented. In addition, a comprehensive systematical evaluation was conducted in two distinct adversarial domains. The results show that even the most straightforward heuristics can produce virtually optimal action selection policies in much fewer episodes, significantly improving the performance of the HAMRL over vanilla RL algorithms.

Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents

This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning algorithm, the Heuristically Accelerated Q---Learning (HAQL). This algorithm allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q---Learning. A heuristic function that influences the choice of the actions characterizes the HAQL algorithm. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results show that even very simple heuristics enhances significantly the performance of the agents.

Using cases as heuristics in reinforcement learning: a transfer learning application

In this paper we propose to combine three AI techniques to speed up a Reinforcement Learning algorithm in a Transfer Learning problem: Case-based Reasoning, Heuristically Accelerated Reinforcement Learning and Neural Networks. To do so, we propose a new algorithm, called L3, which works in 3 stages: in the first stage, it uses Reinforcement Learning to learn how to perform one task, and stores the optimal policy for this problem as a case-base; in the second stage, it uses a Neural Network to map actions from one domain to actions in the other domain and; in the third stage, it uses the case-base learned in the first stage as heuristics to speed up the learning performance in a related, but different, task. The RL algorithm used in the first phase is the Q-learning and in the third phase is the recently proposed Case-based Heuristically Accelerated Q-learning. A set of empirical evaluations were conducted in transferring the learning between two domains, the Acrobot and the Robocup 3D: the policy learned during the solution of the Acrobot Problem is transferred and used to speed up the learning of stability policies for a humanoid robot in the Robocup 3D simulator. The results show that the use of this algorithm can lead to a significant improvement in the performance of the agent.

Using Transfer Learning to Speed-Up Reinforcement Learning: A Cased-Based Approach

Reinforcement Learning (RL) is a well-known technique for the solution of problems where agents need to act with success in an unknown environment, learning through trial and error. However, this technique is not efficient enough to be used in applications with real world demands due to the time that the agent needs to learn. This paper investigates the use of Transfer Learning (TL) between agents to speed up the well-known Q-learning Reinforcement Learning algorithm. The new approach presented here allows the use of cases in a case base as heuristics to speed up the Q-learning algorithm, combining Case-Based Reasoning (CBR) and Heuristically Accelerated Reinforcement Learning (HARL) techniques. A set of empirical evaluations were conducted in the Mountain Car Problem Domain, where the actions learned during the solution of the 2D version of the problem can be used to speed up the learning of the policies for its 3D version. The experiments were made comparing the Q-learning Reinforcement Learning algorithm, the HAQL Heuristic Accelerated Reinforcement Learning (HARL) algorithm and the TL-HAQL algorithm, proposed here. The results show that the use of a case-base for transfer learning can lead to a significant improvement in the performance of the agent, making it learn faster than using either RL or HARL methods alone.

Transferring knowledge as heuristics in reinforcement learning

The goal of this paper is to propose and analyse a transfer learning meta-algorithm that allows the implementation of distinct methods using heuristics to accelerate a Reinforcement Learning procedure in one domain (the target) that are obtained from another (simpler) domain (the source domain). This meta-algorithm works in three stages: first, it uses a Reinforcement Learning step to learn a task on the source domain, storing the knowledge thus obtained in a case base; second, it does an unsupervised mapping of the source-domain actions to the target-domain actions; and, third, the case base obtained in the first stage is used as heuristics to speed up the learning process in the target domain.A set of empirical evaluations were conducted in two target domains: the 3D mountain car (using a learned case base from a 2D simulation) and stability learning for a humanoid robot in the Robocup 3D Soccer Simulator (that uses knowledge learned from the Acrobot domain). The results attest that our transfer learning algorithm outperforms recent heuristically-accelerated reinforcement learning and transfer learning algorithms.

Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case-Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HAMRL is a subset of MRL that makes use of a heuristic function H derived from a case base, in a Case-Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Minimax--Q is also proposed and a set of empirical evaluations were conducted in a simulator for the Littmans robot soccer domain, comparing the three solutions for this problem: MRL, HAMRL and CB-HAMRL. Experimental results show that using CB-HAMRL, the agents learn faster than using RL or HAMRL methods.

Market-based dynamic task allocation using heuristically accelerated reinforcement learning

This paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in real-time. The performance of the task allocation mechanism is evaluated and compared in different implementation variants, and results show that the proposed MRTA system significantly increases the team performance, when compared to pre-programmed team behavior algorithms.