Robot pain: a speculative review of its functions

Given the scarce bibliography dealing explicitly with robot pain, this chapter has enriched its review with related research works about robot behaviours and capacities in which pain could play a role. It is shown that all such roles ¿ranging from punishment to intrinsic motivation and planning knowledge¿ can be formulated within the unified framework of reinforcement learning.Peer ReviewedPostprint (author's final draft

Reinforcement Learning for Nash Equilibrium Generation

Copyright © 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.We propose a new conceptual multi-agent framework which, given a game with an undesirable Nash equilibrium, will almost surely generate a new Nash equilibrium at some predetennined, more desirable pure action profile. The agent(s) targeted for reinforcement learn independently according to a standard model-free algorithm, using internally-generated states corresponding to high-level preference rankings over outcomes. We focus in particular on the case in which the additional reward can be considered as resulting from an internal (re-)appraisal, such that the new equilibrium is stable independent of the continued application of the procedure

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards

Intrinsic rewards were introduced to simulate how human intelligence works; they are usually evaluated by intrinsically-motivated play, i.e., playing games without extrinsic rewards but evaluated with extrinsic rewards. However, none of the existing intrinsic reward approaches can achieve human-level performance under this very challenging setting of intrinsically-motivated play. In this work, we propose a novel megalomania-driven intrinsic reward (called mega-reward), which, to our knowledge, is the first approach that achieves human-level performance in intrinsically-motivated play. Intuitively, mega-reward comes from the observation that infants' intelligence develops when they try to gain more control on entities in an environment; therefore, mega-reward aims to maximize the control capabilities of agents on given entities in a given environment. To formalize mega-reward, a relational transition model is proposed to bridge the gaps between direct and latent control. Experimental studies show that mega-reward (i) can greatly outperform all state-of-the-art intrinsic reward approaches, (ii) generally achieves the same level of performance as Ex-PPO and professional human-level scores, and (iii) has also a superior performance when it is incorporated with extrinsic rewards

The motivational atmosphere in youth sport: coach, parent, and peer influences on motivation in specializing sport participants

This study qualitatively examined the motivationally relevant behaviors of key social agents in specializing sport participants. Seventy-nine participants (9-18 years old) from 26 sports participated in semi-structured focus-groups investigating how coaches, parents, and peers may influence motivation. Using a critical-realist perspective, an inductive content-analysis indicated that specializing athletes perceived a multitude of motivationally-relevant social cues. Coaches’ and parents’ influences were related to their specific roles: instruction/assessment for coaches, support-and-facilitation for parents. Peers influenced motivation through competitive behaviors, collaborative behaviors, evaluative communications, and through their social relationships. The results help to delineate different roles for social agents in influencing athletes' motivation