Скачать или смотреть [Hindi] Reinforcement Learning

Hi friends! In this video, we will discuss reinforcement learning (RL), a popular setting in machine learning.

Reinforcement learning is a framework for sequential decision making where an agent interacts with an environment to learn an optimal policy. The agent takes actions based on the observed state of the environment and receives rewards as feedback. The objective is to maximize the cumulative reward obtained over a sequence of actions.

The agent's behavior is defined by a policy, which is a function that maps states to probabilities of selecting each possible action. The policy is updated based on the received rewards, with the aim of improving its performance over time. Different types of rewards can be used to shape the agent's behavior, guiding it towards desired outcomes.

To find the optimal policy, various reinforcement learning algorithms can be employed. One common approach is to start with a random policy and iteratively improve it through trial and error. The agent takes actions, receives rewards, and updates its policy accordingly, with the goal of increasing the probabilities of actions that lead to high rewards and decreasing the probabilities of actions that lead to low rewards. Through this process, the agent gradually converges towards an optimal policy.

Reinforcement learning differs from other learning settings, such as supervised learning, unsupervised learning, and self-supervised learning, in several ways. In supervised learning, the agent learns from labeled training data, whereas in reinforcement learning, there is no specific training dataset. The agent learns by interacting with the environment and collecting data through trial and error. Reinforcement learning also differs from unsupervised learning, where the algorithm learns patterns from unlabeled data, and self-supervised learning, where labels can be extracted from the data itself.

In terms of evaluation, supervised and self-supervised learning can be evaluated using metrics like accuracy or mean squared error by comparing predicted labels with true labels. In unsupervised learning, evaluation approaches specific to the task are used. In reinforcement learning, the performance of the agent is typically evaluated based on the total reward obtained by the learned policy.

Reinforcement learning provides a powerful framework for tackling sequential decision-making problems, allowing agents to learn and adapt their behavior through interaction with the environment. It has been successfully applied in various domains, including game playing, robotics, and natural language processing (for systems like ChatGPT).


Keywords: Sequential decision making, Agent-environment loop, Reinforcement learning, Policy, Rewards, Optimal policy, Training and inference, Interactions, Trial and error, Cumulative reward, Random policy, Convergence, Supervised learning, Unsupervised learning, Self-supervised learning, Evaluation, Game playing, Robotics, Natural language processing, Adaptive behavior.