Reinforcement Learning: Neurorobotics Introduction

Description

This lecture introduces the concept of reinforcement learning applied to neurorobotics, focusing on the implementation of SARSA algorithm in a simulated environment. The instructor presents the adaptation of Self-Organizing Maps for SARSA, the state-action-reward-state-action model, and the exploration-exploitation trade-off in robot decision-making. The lecture concludes with a demonstration of SARSA training in a simulated environment, showcasing the learning process and the achieved rewards.

In MOOCs (2)

Official source

https://mediaspace.epfl.ch/media/0_pfy9q2kg

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Related concepts (9)

Reinforcement learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs from supervised learning in not needing labelled input/output pairs to be presented, and in not needing sub-optimal actions to be explicitly corrected.

Q-learning

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations. For any finite Markov decision process (FMDP), Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.

Temporal difference learning

Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods, and perform updates based on current estimates, like dynamic programming methods. While Monte Carlo methods only adjust their estimates once the final outcome is known, TD methods adjust predictions to match later, more accurate, predictions about the future before the final outcome is known.

Age of Discovery

The Age of Discovery or the Age of Exploration, part of the early modern period and largely overlapping with the Age of Sail, was a period from approximately the 15th century to the 17th century, during which seafarers from a number of European countries explored, colonized, and conquered regions across the globe. The extensive overseas exploration, particularly the European colonisation of the Americas, with the Spanish and Portuguese at the forefront, later joined by the Dutch, English, and French, marked an increased adoption of colonialism as a government policy in several European states.

Factory (trading post)

Factory was the common name during the medieval and early modern eras for an entrepôt – which was essentially an early form of free-trade zone or transshipment point. At a factory, local inhabitants could interact with foreign merchants, often known as factors. First established in Europe, factories eventually spread to many other parts of the world. The origin of the word factory is (feitoria; factorij; factorerie, comptoir).