A Bayesian Approach to Imitation in Reinforcement Learning
نویسندگان
چکیده
In multiagent environments, forms of social learning such as teaching and imitation have been shown to aid the transfer of knowledge from experts to learners in reinforcement learning (RL). We recast the problem of imitation in a Bayesian framework. Our Bayesian imitation model allows a learner to smoothly pool prior knowledge, data obtained through interaction with the environment, and information inferred from observations of expert agent behaviors. Our model integrates well with recent Bayesian exploration techniques, and can be readily generalized to new settings.
منابع مشابه
A Bayesian Model of Imitation in Infants and Robots
Learning through imitation is a powerful and versatile method for acquiring new behaviors. In humans, a wide range of behaviors, from styles of social interaction to tool use, are passed from one generation to another through imitative learning. Although imitation evolved through Darwinian means, it achieves Lamarckian ends: it is a mechanism for the inheritance of acquired characteristics. Unl...
متن کاملReinforcement and Imitation Learning via Interactive No-Regret Learning
Recent work has demonstrated that problems– particularly imitation learning and structured prediction– where a learner’s predictions influence the inputdistribution it is tested on can be naturally addressed by an interactive approach and analyzed using no-regret online learning. These approaches to imitation learning, however, neither require nor benefit from information about the cost of acti...
متن کاملEmbodied imitation-enhanced reinforcement learning in multi-agent systems
Imitation is an example of social learning in which an individual observes and copies another’s actions. This paper presents a new method for using imitation as a way of enhancing the learning speed of individual agents that employ a well-known reinforcement learning algorithm, namely Q-learning. Compared to other research that uses imitation with reinforcement learning, our method uses imitati...
متن کاملMachine learning for developmental robotics
Developmental robotics is the process whereby a robot incrementally acquires more and more complex cognitive skills. This approach draws inspiration from biology to tackle the ultimate goal of robotics, i.e. intelligent robust machines operating in open ended environments. The main idea is to equip the robot with a set of predefined (pre-programmed) skills and then follow learning processes to ...
متن کاملGenerative Adversarial Imitation Learning
Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert’s cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a...
متن کامل