- Pairwise Learning
- Pairwise Ranking
- Parity Learning
- Partial Least Squares Regression
- Pattern Recognition
- Perceptron Learning Algorithm
- Permutation Invariance
- Point Cloud Processing
- Policy Gradient Methods
- Policy Search
- Pooling Layers
- Positive-Definite Kernels
- Positive-Unlabeled Learning
- Pre-trained Models
- Precision and Recall
- Predictive Analytics
- Predictive Maintenance
- Predictive Modeling
- Preference Elicitation
- Preference Learning
- Principal Component Analysis (PCA)
- Privacy Preserving Data Mining
- Privacy Preserving Machine Learning
- Probabilistic Graphical Models
- Probabilistic Matrix Factorization
- Probabilistic Programming
- Probabilistic Time Series Models
- Prompt Engineering
- Prototype-based Learning
- Proximal Policy Optimization (PPO)
- Pruning
What is Proximal Policy Optimization (PPO)
Proximal Policy Optimization (PPO) - An Introduction
Proximal Policy Optimization (PPO) is a widely-implemented algorithm in the field of Reinforcement Learning (RL) that has shown excellent performance in several challenging benchmark environments. This algorithm was introduced in the year 2017 by Schulman et al. from OpenAI, and since then, it has become one of the go-to algorithms in the field of RL. The primary reason behind the popularity of PPO is its simplicity and effectiveness. This algorithm has proved to be robust in a wide range of RL tasks and can quickly adapt to changing environments. In this article, we will dive deep into the fundamentals of PPO and understand how it works. Background Before we dive into the technical details of the PPO algorithm, let us first understand some background concepts related to RL. Reinforcement learning is a type of machine learning that focuses on learning by interacting with an environment. In RL, the agent learns to make optimal decisions by taking actions in the environment and receiving feedback in the form of rewards. The agent's goal is to maximize the cumulative reward received over time. The RL framework consists of three main components: the environment, the agent, and the policy. The environment is the external entity with which the agent interacts. The agent is the decision-maker that learns to make optimal decisions by receiving feedback from the environment. Finally, the policy is the agent's decision-making process that maps states to actions. Markov Decision Process To formally define the RL problem, we use the Markov Decision Process (MDP). An MDP is defined as a tuple (S, A, P, R, γ), where:- S is the set of possible states.
- A is the set of possible actions.
- P is the transition probability function, i.e., P(s'|s,a) is the probability of transitioning to state s' from state s by taking action a.
- R is the reward function, i.e., R(s,a,s') is the reward received when transitioning from state s to state s' by taking action a.
- γ is the discount factor that determines the importance of future rewards.
- Δθ is the change in policy parameters.
- α is the learning rate.
- π(a|s) is the probability of taking action a in state s according to the policy.
- Q(s,a) is the state-action value function that estimates the expected cumulative reward starting from state s, taking action a, and following the policy thereafter.
- L_CLIP(θ) is the clipped objective function.
- r(θ) is the ratio of the new policy to the old policy, i.e., r(θ) = π_new(a|s) / π_old(a|s).
- clip(x,a,b) is a function that clips x between the values a and b.
- ε is a hyperparameter that controls the size of the policy update.
- A(s,a) is the advantage function that measures how much better the action a is compared to the average action in state s.
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