Reinforcement learning (RL) is the machine learning method that trains an agent to make decisions through environmental interaction. The agent performs the actions, receives the reward or penalty based on performance, and automatically adjusts the overall strategy to maximise the cumulative reward. Similarly, reinforcement learning in robotics refers to the technique of training the robot using trial and error method instead of explicitly programming it. Unlike the supervised learning method where predefined answers in training datasets are used, reinforcement learning in robotics relies on practical experiences and training.
This is a step-by-step guide for reinforcement learning in robotics in which you will explore the basic introduction of the RL in robotics, its key components, and the steps involved in the reinforcement learning process in robotics. Moreover, it also covers some important frequently asked questions that will clarify the important concept regarding this topic.
Introduction to Reinforcement Learning in Robotics
Reinforcement learning (RL) in robotics is one of the machine learning techniques in which the robot learns through environmental interactions instead of relying on pre-defined explicit programs. The robots perform the actions and receive feedback based on their performance. The system is defined in a way that the robot has multiple outcomes from its actions and performs all the expected actions one after the other. The reinforcement learning algorithm uses the feedback in the form of reward or punishment and the robot learns from every state. It is designed to try its best to get the maximum positive reward to reach its goal. As a result, the robot gradually improves its performance, and at the end of this training, provides the most refined and improved performance. This enables the robot to solve complex and unseen tasks autonomously in different environmental scenarios.
Key Components of Reinforcement Learning in Robotics
The reinforcement learning technique in robotics operates on the training of robots through the trial and error method. Beyond the agent’s interaction with the environment, there are four basic sub-elements of this method defined below:
Policy
The policy in the RL in robotics is the strategy a robot adopts to determine its actions based on the current environmental state. In this policy, the robot maps the environmental state to the specific action to get the maximum reward and avoid the punishment.
Reward Function
The reward function of RL in robotics is the mathematical model to provide feedback based on its actions performed in the environment. This function assigns the numerical value in the form reward to every possible action (or the action sequence) to train the robot to get the maximum commutative reward over time. As a result, the robot’s main adjective is to increase the compensation and decrease the punishment to get a maximum reward at the end of every action.
Value Function
The value function is the mathematical representation of the long-term cumulative reward the robot can expect to get long-term benefit from the given state and is in the form of a mathematical function. The two most common value functions for robots are mentioned here:
- Vπ(s) = Eπ[ Σ from t=0 to ∞ γ^t * rt | s0=s]
- Qπ(s, a) = Eπ[ Σ from t=0 to ∞ γ^t * rt | s0=s, a0=a]
Here,
π= Specific policy
γ= Discount factor that determines how much future reward is expected in the state
rt= Reward at the step t
s= state
a= action performed
Model
A model is the internal representation of the model to understand the environmental behavior. The presence of this component in the RL is based on the training stage of the robot. The robot can predict the expected reward from a particular stage. His component is only present in the model-based RL algorithms.
Steps in Reinforcement Learning in Robotics
To make the robot artificially intelligent(AI), we need to follow these steps for the reinforcement learning process in robotics to learn the optimal actions through environmental interaction:
Step 1: Problem Definition and Objective Setting
Before diving into reinforcement learning in robotics, the experts specify the task the robot has to learn. The task may range from simple to complex according to the user’s requirements. Based on the task, the objective is given to the robot for learning.
Examples
- Balance on two wheels
- Pick up the object and pass the path without hitting any obstacle
- Choose the right path to reach a particular place in minimum time without any damage
Step 2: Define Environment
The next step is to define the environment for the robot to introduce the operating boundaries. It can be the physical or the simulated environment that contains all the state spaces which are defined as:
“The state space is the set of all the possible states a robot can be within the particular environment for the particular problem.”
Once the environment and states are defined, the robot chooses the right state to get the maximum reward.
Step 3: Defining the Action State
The action state is the set of all the actions and steps a robot can perform in a particular environment. These actions may be moving (forward, backward, left, or right), speed adjustment, choosing the right path, or any other related task to the particular problem. Choosing the right action state is the ultimate goal of this whole training.
Step 4: Designing the Reward Function
This is a critical step and involves the setting of the feedback system. This is the criteria that will show the robot how close or far it is to the ultimate goal. Based on this function, the robot learns the particular step or action that brings it close to the environment. The following logic is applied in the reward function output:
- The positive reward will bring the robot close to its goal. For instance, the +10 points for the successful completion of the step or covering a particular distance.
- The penalties are applied for performing undesirable steps. For instance, -5 for hitting an obstacle or dropping the object.
- The negative marking on taking a longer time than expected to reach a particular place. For instance, if the robot is late but successfully completes the task, it would have -1 from the total score.
Step 5: Choosing Reinforcement Learning Algorithm in Robotics
Once all the basics are complete, the involvement of the right machine-learning algorithm is uncomplicated. Some commonly used reinforcement learning algorithms applied in robotics are:
Q-Learning
This algorithm involves the use of a table to store the outputs for a particular action-state pair to the future reward. The table in this method is called the Q table whereas the values are referred to as the Q values. The robot continuously updates the table according to the feedback of action over time as a record and learns to adopt the step for maximum reward.
Deep Q Network
The deep Q network is also known as the DQN and is the extension of the Q-learning but it uses the neural network instead of a table. This is particularly useful when the space state is too large and complex and can not be fit into the table.
Policy Gradient Method
This method directly optimizes the robot’s policy instead of estimating the Q values. The robot learns from the expected probabilities of every action state and the reward associated with it.
Step 6: Deployment and Improvement
Once the algorithm is tested, the robot undergoes multiple trials and learns from its own performance. This training is based on real-world scenarios and at the end of every trial, the results are refined and the robot performs better to get the maximum reward.
Frequently Asked Questions
Why Reinforcement Learning is Preferred for Robotics?
The reinforcement learning algorithms are applied to cases where programming the possible outcomes is not possible in practical scenarios. Usually, complex behaviors are expected from the robots therefore, reinforcement in learning is a better option than programming or supervised learning.
What are the challenges in RL in robotics?
The particular challenges are involved in the RL learning in robotics:
- Efficiency and Performance:
The RL is a data-driven technique and is sensitive to the specific selection of the parameters. The environmental factors may be different for every case so it is difficult to get the optimal change in the environment. It is a time-consuming process that may or may not provide the expected output.
- Sim-to-Real Gap:
The simulation environment is adopted for the training process to provide a fast, efficient, and cost-friendly environment to the robot but the real-time scenarios are always different from the simulation environment. This is called the sim to the real gap and the training process is not always one hundred percent effective.
- Data and Efficiency:
Reinforcement learning requires a large amount of data and millions of environmental interactions for the training process. As a result, it can’t be applied to complex processes because of time and cost restrictions. Although, people are working on different methods and algorithms to minimise the time.
Can RL train multiple robots at a time?
Yes, the same RL system can simultaneously be applied to multiple robots at a time that can be trained through completion or competing with each other within the task. This system is called multi-agent reinforcement learning (MARL) and every robot performs its duty to optimize the strategy and create the best policy.
Summary
Reinforcement learning in robotics is the process in which the robots are not programmed with the possible outcomes but are unsupervised and learned from different actions using the trial and error method. Mainly. The whole process involves the task definition, policy-making, algorithm choice, and training process. Multiple machine learning algorithms are designed to be applied in such learning and the choice depends on the task type. It is a popular and efficient technique through which robots can mimic human behaviour and get the reward from the most accurate state.
Author’s Name: Alice Brown
Author’s bio: I am a Computer Engineer and a part-time hobbyist, who believes that only research & technology can make this world a better place.