Title: Overview on Reinforcement Learning for Robotics. Abstract: Reinforcement Learning(RL) offers robotics tasks a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors.[1]
Reinforcement Learning is an branch of Machine Learning inspired by behaviorist psychology[wiki- Reinforcemenr Learning], concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.
The basic idea of Reinforcement Learning is to obtain a policy that extract more reward from the environment by picking actions given a state.
By policy, we mean a decision maker (Agent) that decide on an action based on some parameterized rules given an input observation of environment (State). The policy can be a set of weight that linearly combine the features in a state or different structured Neural Network. The environment in Reinforcement Learning context provide the agent a new state and reward immediately after the agent takes a specific action.
From a more broad view, the Machine Learning method was mainly three folds. The Supervised Learning, Semi-supervised Learning and Unsupervised Learning. The supervised learning network was trained given a dataset including the observation data and the corresponding categorization. The latter was given a dataset that no classification is label to the observation data. For reinforcement Learning, it is more close to supervised learning, while its label is obtained by exploring the environment and get feedback (reward, r) from it. The RL algorithm marks the policy that generates the highest score as the training target and make small change of its parameters (or weights, θ) towards that policy until the policy converge.
In this report, we mainly focus on the methods of Reinforcement Learning methods for robotics.