In reinforcement learning, an on-policy algorithm is a type of algorithm that learns from its own experience. This means that the algorithm uses the same policy to generate actions and to update its policy. Some examples of on-policy algorithms include:
On-policy algorithms have several advantages over off-policy algorithms. First, they are typically more stable and less prone to overfitting. Second, they can be used to learn from a single trajectory, which can be useful in cases where data is scarce.
However, on-policy algorithms also have some disadvantages. First, they can be slower to learn than off-policy algorithms. Second, they can be more sensitive to the initial policy.
In general, on-policy algorithms are a good choice for problems where data is scarce or where stability is important. However, off-policy algorithms may be a better choice for problems where speed or robustness is more important.
Here is a table that summarizes the key differences between on-policy and off-policy algorithms:
| Feature | On-policy | Off-policy |
|---|---|---|
| Uses the same policy to generate actions and to update the policy | Yes | No |
| Typically more stable and less prone to overfitting | Yes | No |
| Can be used to learn from a single trajectory | Yes | No |
| Slower to learn | Yes | No |
| More sensitive to the initial policy | Yes | No |
On-policy: the agent learned and the agent interacting with the environment is the same.
off-policy: the agent learned and the agent interacting with the environment is different.
翻译过来就是：
On-policy: 学习到的agent以及和环境进行互动的agent是同一个agent
Off-policy: 学习到的agent以及和环境进行互动的agent是不同的agent
直观理解就是：
On-policy：相当于你在打游戏，通过实战变强。
Off-policy：相当于你在看直播，通过观摩变强。
On-Policy can be used for model-based and model-free reinforcement learning however, off-policy is used for model-free reinforcement learning algorithms. An example of on-policy is SARSA, and an example of off-policy is Q-learning.
InstructGPT uses a policy gradient reinforcement learning algorithm called Proximal Policy Optimization (PPO). PPO is an off-policy algorithm, which means that it can learn from experience collected from previous policies. This makes it more efficient than on-policy algorithms, which can only learn from experience collected from the current policy.
PPO works by iteratively adjusting the policy to maximize the expected reward. In each iteration, PPO samples a batch of data from the replay buffer and calculates the advantage of each action. The advantage is a measure of how much better or worse an action was than the expected action. PPO then uses the advantage to update the policy in a way that makes it more likely to take actions that have a high advantage.
PPO has been shown to be very effective for training reinforcement learning agents. It is used in a variety of applications, including robotics, game playing, and natural language processing.
In the case of InstructGPT, the policy is a stochastic policy that generates text. The reward is a measure of how well the generated text matches the instructions. PPO is used to update the policy in a way that makes it more likely to generate text that matches the instructions.
Here are some of the benefits of using PPO for reinforcement learning:
Here are some of the drawbacks of using PPO for reinforcement learning:
Overall, PPO is a powerful reinforcement learning algorithm that can be used to train agents in a variety of environments. It is more efficient and stable than other off-policy algorithms, but it can be difficult to tune and computationally expensive to train.