Motivated by the scarcity of accurate payoff feedback in practical applications of game theory, we examine a class of learning dynamics where players adjust their choices based on past payoff observations that are subject to noise and random disturbances First, in the single-player case (corresponding to an agent trying to adapt to an arbitrarily changing environment), we show that the stochastic dynamics under study lead to no regret almost surely, irrespective of the noise level in the player's observations In the multi-player case, we find that dominated strategies become extinct and we show that strict Nash equilibria are stochastically stable and attracting; conversely, if a state is stable or attracting with positive probability, then it is a Nash equilibrium Finally, we provide an averaging principle for 2-player games, and we show that in zero-sum games with an interior equilibrium, time averages converge to Nash equilibrium for any noise level

On the robustness of learning in games with stochastically perturbed payoff observations

https://hal.inria.fr/hal-01098494/document

Generalizing the operation of robots in dynamical environments regardless of the task complexity is one of the ultimate goals of robotics researchers. Learning from demonstration approaches supported by transfer learning and user feedback offer a remarkable solution to achieve generalization. The main idea behind such approaches is teaching robots new skills with human instructors and training parametric models with data from demonstrations to achieve and update the desired skills under changing conditions. Herein, the applications of skill transfer with reinforcement learning algorithms and the effect of user experience (UX) on learning from demonstration approaches are reviewed. This review outlines the importance of considering and evaluating UX during human–robot interaction and, especially, robot teaching. A detailed view on the relations between robot learning and UX is provided and approaches for future improvements are derived. Finally, adaptive autonomy sharing between the robot and the user during teaching is presented as a promising approach to enhance the interaction by exploiting user feedback. In the long run, interactive and user‐centered human–robot skill transfer is expected to reduce cognitive and physical load of the user. Discussion on future research questions aiming to improve learning process and semiautonomous behavior concludes the review.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aisy.202000247

Interactive Human–Robot Skill Transfer: A Review of Learning Methods and User Experience

Analysis, simulation and implementation of wireless TCP flows with forward error correction

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Analysis, simulation and implementation of wireless TCP flows with forward error correction