Recent Advances In Reinforcement Learning

Reinforcement learning is an area of machine learning that focuses on taking suitable action for maximum reward in a particular situation.

As an example, picture an agent and an object. The various paths to the object are littered with hurdles. When the agent reaches the object, it is rewarded. Every move that gets the agent closer to the object is a positive experience whereas any wrong moves will take the agent further away from the object and the reward.

There are several terms and phrases used when discussing reinforcement learning. An agent is the assumed entity that performs actions in an environment, which is the scenario or situation. The reward is an immediate return given to the agent when a specific task is performed and policy is the strategy applied by the agent to decide its next action.

A notable characteristic of reinforcement learning is the lack of a supervisor. Sequential decision-making and delayed feedback are a few more characteristics. It is also important to note that the agent receives data based on its previous action.

There two kinds of reinforcement learning; Positive and Negative. Positive reinforcement maximizes performance and sustains change for a long time but excessive positive reinforcement can diminish the results of the process.

Negative reinforcement is useful when defining the minimum standard of performance but the drawback is that it only provides enough to meet the minimum behavior.

A reinforcement learning company will use two main models of learning. One is the Markov Decision Process, which uses parameters like a set of actions, set of states, reward, policy, and value to get a solution.

Q-Learning is a method of supplying information so the agent is informed of the action it should take.

There are various applications of reinforcement learning. It can be used for industrial automation and business strategy planning. A machine learning consultancy and AI app development company may also use reinforcement learning when developing their products.

Over the years, reinforcement learning has been subject to constant improvements and changes. The process is being constantly developed to ensure better and more accurate results.

There have been several studies carried out on the process and research papers like ‘Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor’ by Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine published in 2018 have been important to the field.

If you take a look at ‘Temporal Difference Models: Model-Free Deep RL for Model-Based Control’ by Vitchyr Pong, Shixiang Gu, Murtaza Dalal, and Sergey Levine, the paper shows that Temporal Difference Models (TDM) combine the benefits of model-free and model-based algorithms.

This is done by achieving asymptotic performance close to the model-free algorithm and learning as quickly as a model-based method.

In ‘Addressing Function Approximation Error in Actor-Critic Methods’ authors Scott Fujimoto, Herke van Hoof, and David Meger found that high variance contributed to overestimation bias, leads to a noisy gradient for policy updates, and results in lower performance.

To address high variance, the authors introduced the Twin Delayed Deep Deterministic policy gradient algorithm (TD3), which is based on the Deep Deterministic Policy Gradient (DDPG) algorithm but includes several important modifications.

Another important advancement in reinforcement learning is hierarchical guidance, an algorithmic framework that leverages the hierarchical structure of the underlying problem.

This was introduced by Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, and Hal Daumé III in their 2018 paper ‘Hierarchical Imitation and Reinforcement Learning.

Recent research papers on reinforcement learning include those presented at the 2020 Conference on Neural Information Processing Systems (NeurIPS).

In the paper titled ‘Novelty Search in Representational Space for Sample Efficient Exploration’, Ruo Yu Tao, Vincent François-Lavet, and Joelle Pineau introduced, “a new approach for efficient exploration which leverages a low-dimensional encoding of the environment learned with a combination of model-based and model-free objectives.”

At NeurIPS 2020, Alekh Agarwal, Sham Kakade, Akshay Krishnamurthy, and Wen Sun introduced FLAMBE, which engages in exploration and representation learning for provably efficient reinforcement learning in low-rank transition models.

These studies have played an important role in pushing reinforcement learning further and enhancing the application of these processes in various sectors.