1. Introduction
On-policy reinforcement learning (RL) methods are fundamental to environments where continuous feedback from the agent's current policy is crucial. These methods involve training agents using data generated by the policy they aim to improve, ensuring that the updates directly reflect the current behavior of the agent. While on-policy methods are less sample-efficient than off-policy techniques, they are valued for their stability and ability to adapt seamlessly to changes in the environment.
This lesson will introduce the principles of on-policy RL methods, focusing on their strengths and trade-offs. We will discuss key algorithms such as REINFORCE, Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), and Trust Region Policy Optimization (TRPO), emphasizing their design and application in various scenarios.
2. Compute and Sample Efficiency: On-Policy vs. Off-Policy Methods
2a. On-Policy Methods:
- In on-policy RL, the agent collects data using its current policy and immediately uses it to update the same policy.
- This approach ensures that the learned policy always reflects the most recent data, making on-policy methods highly suitable for non-stationary environments.
- However, on-policy methods suffer from sample inefficiency because each experience can be used only once. This requires the agent to interact with the environment repeatedly, which can be computationally expensive and time-intensive.
2b. Comparison with Off-Policy Methods:
- Unlike on-policy methods, off-policy approaches reuse stored experiences multiple times for updates, improving sample efficiency.
- While off-policy methods like Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC) excel in continuous action spaces, on-policy methods prioritize policy stability over efficiency, making them ideal for tasks where maintaining the integrity of the current policy is essential.
3. Key On-Policy Algorithms
On-policy methods are widely used in reinforcement learning due to their simplicity and direct policy improvement. Below are the most notable algorithms:
3a. REINFORCE (Monte Carlo Policy Gradient)
- Overview: REINFORCE is a foundational on-policy algorithm that directly optimizes the policy by computing gradients based on complete episode rewards.
- Key Features:
- Simplicity: It uses Monte Carlo estimates of returns without requiring a value function.
- Drawbacks: High variance in gradient estimates makes it less suitable for complex environments.
3b. Advantage Actor-Critic (A2C)
- Overview: A2C introduces a value function (critic) to reduce the variance of policy gradient updates, making training more stable.
- Key Features:
- Advantage Function: A2C uses the difference between actual returns and the baseline (value function) to compute the advantage, ensuring more informed updates.
- Synchronous Updates: A2C updates the policy synchronously across multiple environments, improving training stability.
3c. Trust Region Policy Optimization (TRPO)
- Overview: TRPO is a more advanced on-policy method designed to ensure that updates to the policy are safe and stable.
- Key Features:
- Trust Region: Restricts policy updates to a "trust region" to prevent overly large changes that could destabilize learning.
- Suitability: TRPO is ideal for environments requiring precise and stable policy updates, though it is computationally expensive.
3d. Proximal Policy Optimization (PPO)
- Overview: PPO simplifies TRPO by introducing clipped surrogate objectives, making it computationally efficient while maintaining stability.
- Key Features:
- Clipped Objective Function: Prevents large policy updates, ensuring stability.
- Versatility: PPO is one of the most widely used RL algorithms due to its balance of performance and simplicity.
4. Why Choose On-Policy Methods?
On-policy methods are best suited for environments where:
- Policy Stability is critical: The agent's behavior must remain stable and consistent with its learning policy.
- Non-Stationarity is a concern: The environment changes over time, requiring the agent to learn continuously based on current interactions.
- Exploration vs. Exploitation: Policies are often stochastic, naturally encouraging exploration while optimizing expected rewards.
5. Summary
On-policy methods prioritize stable and direct policy improvement by using experiences generated from the current policy. Algorithms like REINFORCE, A2C, TRPO, and PPO are widely used in diverse RL applications, from robotics to gaming, where continuous feedback and stable learning are critical.
In the next lessons, we will delve deeper into these algorithms, starting with REINFORCE, to understand their mechanics and how they can be effectively implemented in real-world scenarios.