Proximal Policy Optimization (PPO) is an advancement in reinforcement learning, a field rooted in psychology and neuroscience. It was developed as an improvement on the Trust Region Policy Optimization (TRPO), an earlier version introduced by John Schulman in 2015. PPO simplifies TRPO’s complex system by using first-order optimization and modifies its objective function with a penalty for substantial policy updates. The training process in PPO involves the agent performing actions based on input and receiving rewards based on these actions in different scenarios. The primary goal is to maximize total rewards across episodes. PPO is efficient, cost-effective, and does not require extensive computation or hyperparameter tuning. It is known for its stability, generalization across a range of tasks, and sample efficiency, which significantly reduces data collection and computational costs.
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Proximal policy optimization (PPO) is an algorithm in the field of reinforcement learning that trains a computer agent's decision function to accomplish difficult tasks. PPO was developed by John Schulman in 2017, and had become the default reinforcement learning algorithm at American artificial intelligence company OpenAI. In 2018 PPO had received a wide variety of successes, such as controlling a robotic arm, beating professional players at Dota 2, and excelling in Atari games. Many experts called PPO the state of the art because it seems to strike a balance between performance and comprehension.[citation needed] Compared with other algorithms, the three main advantages of PPO are simplicity, stability, and sample efficiency.
PPO is classified as a policy gradient method for training an agent’s policy network. The policy network is the function that the agent uses to make decisions. Essentially, to train the right policy network, PPO takes a small policy update (step size), so the agent can reliably reach the optimal solution. A too-big step may direct policy in the false direction, thus having little possibility of recovery; a too-small step lowers overall efficiency. Consequently, PPO implements a clip function that constrains the policy update of an agent from being too large or too small.
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