Augmentation policies in reinforcement learning (RL) are strategies to improve an agent’s generalization by modifying its observations or environment during training. These policies apply transformations to the data the agent interacts with, similar to how image rotations or color shifts are used in supervised learning. The goal is to expose the agent to a wider variety of scenarios, reducing overfitting to specific training conditions. For example, in a robot navigation task, augmentations might involve altering lighting, adding visual noise, or randomizing camera angles in simulated training environments. By doing this, the agent learns to handle variations it might encounter in real-world deployment.
A key consideration is ensuring that augmentations preserve the underlying dynamics of the environment. For instance, flipping an image horizontally in a game like Pong would reverse the direction of the paddle’s movement. If the augmentation isn’t accounted for in the action space, the agent might take incorrect actions. To address this, some methods adjust the policy’s output to align with the transformation. For example, if an image is flipped, the “move left” action could be swapped with “move right” during training. Another approach is using domain randomization, where parameters like friction, object textures, or gravity are varied in simulation. This forces the agent to adapt to diverse physics without breaking the environment’s core rules. In robotics, training with randomized grip strengths or object sizes helps policies generalize to unseen physical conditions.
Augmentations can also be applied to the agent’s experience replay buffer. When sampling past transitions, states are modified (e.g., adding noise to sensor data) to create synthetic but plausible variations. For visual RL tasks, techniques like random cropping, color jitter, or frame stacking are common. However, care must be taken to avoid invalid states—for example, cropping an image too aggressively might remove critical game elements. Successful implementations, such as those in Procgen benchmarks, show that agents trained with these augmentations perform better in unseen levels. The effectiveness of augmentation policies depends on balancing diversity with realism, ensuring the agent learns robust features without misleading its understanding of the environment’s dynamics.