RL reinforcement learning

Reinforcement Learning (RL) is a subfield of machine learning that focuses on an agent learning to make decisions in an environment in order to maximize a notion of cumulative reward. RL is inspired by the way humans and animals learn through trial and error interactions with their surroundings.

In RL, an agent interacts with an environment in a series of discrete time steps. At each time step, the agent observes the current state of the environment and takes an action. The environment responds by transitioning to a new state and providing the agent with a reward signal that indicates the desirability of the agent's action. The agent's goal is to learn a policy, which is a mapping from states to actions, that maximizes the expected cumulative reward over time.

To formalize the RL problem, we use the framework of Markov Decision Processes (MDPs). An MDP is defined by a tuple (S, A, P, R), where:

• S is the set of possible states in the environment.
• A is the set of possible actions the agent can take.
• P is the state transition function, which gives the probability of transitioning to a new state s' when the agent takes action a in state s.
• R is the reward function, which gives the immediate reward the agent receives after taking action a in state s and transitioning to state s'.

The agent's interaction with the environment can be modeled as a sequence of state-action pairs (s, a) and resulting state-reward pairs (s, a, s', r). The agent's goal is to find an optimal policy π* that maximizes the expected cumulative reward:

π* = argmaxΣγ^t * r

where γ is the discount factor (0 ≤ γ ≤ 1) that determines the importance of immediate rewards compared to future rewards, and t is the time step.

Reinforcement learning algorithms can be broadly categorized into model-based and model-free approaches.

Model-based RL:

In model-based RL, the agent builds an explicit model of the environment dynamics (i.e., the state transition function P) and then plans and learns based on that model. The agent uses the learned model to simulate different actions and their outcomes in order to estimate the expected cumulative reward. Planning algorithms such as dynamic programming or Monte Carlo Tree Search can be used to optimize the agent's policy.

Model-free RL:

In model-free RL, the agent directly learns the optimal policy without explicitly building a model of the environment. Model-free algorithms typically use value functions or policy search methods to estimate the expected cumulative reward.

• Value functions: Value functions estimate the expected cumulative reward from a given state or state-action pair. The value function can be represented in different forms, such as the state value function (V(s)) or the action value function (Q(s, a)). Value-based algorithms, such as Q-learning or SARSA, iteratively update the value function based on observed rewards and state transitions to converge to an optimal policy.
• Policy search: Policy search methods directly search for the optimal policy by exploring the action space and evaluating the performance of different policies. The agent uses gradient ascent or other optimization techniques to update the policy parameters based on the observed rewards. Policy gradient algorithms, such as REINFORCE or Proximal Policy Optimization (PPO), fall into this category.

Both model-based and model-free approaches have their own strengths and weaknesses. Model-based methods require accurate models of the environment, which may not always be available or practical to construct. On the other hand, model-free methods can directly learn from interactions with the environment but may require more data and time to converge.

Reinforcement learning has been successfully applied to various real-world problems, including game playing (e.g., AlphaGo), robotics, autonomous driving, recommendation systems, and more. Its ability to learn optimal policies through trial and error makes it a powerful approach for tackling complex decision-making tasks.