Reinforcing the Machines

By: Apurva Chavan, RIG Inc. Intern Researcher

Humans can recognize that they have made a mistake and also learn from their mistakes. This additional knowledge that humans gain from their mistakes ‘reinforces’ them to not make such mistakes again in the future. The same concept can now be applied to machines and it is called ‘Reinforcement Learning’.

Reinforcement Learning (RL) is the science of decision-making. It is about learning the optimal behavior in an environment to obtain the maximum reward. This optimal behavior is learned through interactions with the environment and observations of how it responds. This is similar to children exploring the world around them and learning the actions that help them achieve a goal.

In technical terms, Reinforcement learning is a machine learning (ML)-based training model which enables the agent to discern and understand its environment while taking actions and learning through the trial-and-error method.

Main Components in Reinforcement Learning: –

The Agent – An agent takes an action from a set of all possible actions available to the agent.
The environment (where the agent functions) – The world through which the agent moves and responds to the agent.
The State – A state is a position of the agent in the environment at a particular moment in time i.e., a specific place or an instantaneous configuration.
The policy – The policy is the agent’s strategy to determine the next action based on the current state.
The reward – A reward is the feedback of the action taken by the agent by which we measure the success or failure of the agent.

How does Reinforcement Learning Work?

Reinforcement Learning problems involve an agent exploring an unknown environment to achieve a goal following an optimal policy while collecting the maximum expected cumulative reward. The purpose of RL is to make the agent explore and learn its way through the environment by performing actions to get the maximum reward. It is up to the agent to figure out how to maximize the cumulative reward. Reinforcement Learning works on the principle of exploration and exploitation. The entire process includes starting with the random actions taken by an agent in exploring the environment to find the best action (exploration) till finishing with an optimal path that the agent will take to reach the goal with maximum rewards (exploitation).

RL borrows its framework from the problem of optimal control of the Markov Decision Process (MDP). RL algorithms are of two types, ‘model-free’ and ‘model-based’. In the model-free algorithm, the agent does not build a virtual model of the environment in which it functions in. The agent will experiment on the environment using its actions and seek to learn the consequences of its actions through experience. In the model-based algorithm, the agent tries to understand its environment and creates a virtual model of the environment based on its interactions. Rather than learning from the consequences, the agent prioritizes its predilection based on the model of the environment i.e, the agent will always try to perform an action that gives the maximum rewards without considering the repercussion of that action.

“Model-based methods rely on planning as their primary component, while model-free methods primarily rely on learning.”

Advantages and Application –

Reinforcement learning can be used for a wide range of complex problems which are not possible with other machine learning algorithms. RL is comparatively closer to artificial general intelligence (AGI), as it autonomously explores various possibilities while trying to attain the long-term goal. Other advantages include focusing on the problem as a whole; no separate data collection is needed as the agent collects data on its own. RL can also work in continuously changing, varying and uncertain environments.

Applications of reinforcement learning include RL in gaming, healthcare, security, e-commerce, trading, banking and many more. One of the major applications of RL includes reinforcement learning driven robots in a factory or a warehouse which helps in faster supply of materials and also reduces the burden on human labor.

Challenges –

Implementing Reinforcement Learning in the real world is slow even with its proven ability in solving complex problems in different environments. First, preparing a simulation environment for only specific actions that the agent can perform is a challenging task. Second, the data is generated by an agent after interacting with the environment so the scope of the generated data is limited to that environment itself. On the other hand, the agent needs to have extensive experience in constantly changing environments which are responsible for slowing down the learning process of the agent. Third, as reinforcement learning focuses on achieving long-term rewards it might unwillingly overlook the short-term rewards making the process of determining the optimal policy a bit difficult.

Conclusion –

In recent years, notable progress has been made in the field of Reinforcement Learning. Also, the emergence of MARL (Multi-Agent Reinforcement Learning), wherein multiple agents can operate in the same environment, might help in making the entire learning process faster as the agents will be able to communicate with each other and share their experiences. This will quicken the learning process of the agent in reaching the goal and is an exciting development in this field which may lead to accelerated incorporation of RL in the real world.

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