What are the most common pitfalls in RL?

The most common pitfalls in reinforcement learning (RL) include sparse reward signals, difficulty balancing exploration and exploitation, and high sample inefficiency. These challenges often lead to slow learning, unstable training, or agents that fail to achieve their goals. Understanding these issues is critical for developers designing RL systems.

First, sparse rewards occur when an agent receives feedback only after completing a long sequence of actions, making it hard to associate specific behaviors with outcomes. For example, in a game where the agent only gets a reward upon winning, it may never discover the steps needed to reach that outcome. This is akin to teaching someone to play chess by only telling them whether they won or lost, without explaining which moves were good. Solutions include reward shaping (adding intermediate rewards for subgoals) or intrinsic motivation (encouraging the agent to explore novel states). Without such adjustments, the agent might never learn meaningful strategies, wasting computational resources on random trial and error.

Second, balancing exploration (trying new actions) and exploitation (using known effective actions) is a persistent challenge. If an agent exploits too much, it might miss better strategies—like a robot always taking the same path to avoid short-term obstacles but never finding faster routes. Conversely, excessive exploration can lead to chaotic behavior, such as a self-driving car randomly swerving to test alternatives. Algorithms like epsilon-greedy or Thompson sampling attempt to address this by dynamically adjusting exploration rates. However, tuning parameters like the exploration rate (epsilon) requires careful experimentation. For instance, setting epsilon too low in a maze-solving task might trap the agent in a local optimum, while setting it too high could prevent progress altogether.

Third, RL algorithms often require vast amounts of data to learn effectively, which is impractical in real-world scenarios. Training a robot to grasp objects might demand millions of simulated trials, and transferring that knowledge to the physical world can fail due to differences in sensor data or physics. Techniques like model-based RL (using a learned simulator to plan actions) or imitation learning (copying expert demonstrations) can reduce sample requirements. For example, training a drone to navigate using a simulated environment speeds up learning but risks overfitting to simulation inaccuracies. Developers must also optimize hyperparameters like discount factors or learning rates, as small errors here can destabilize training. A learning rate too high might cause an agent playing Atari games to overshoot optimal policies, while one too low could stall progress entirely.

By addressing these pitfalls through careful reward design, exploration strategies, and efficiency optimizations, developers can build more robust RL systems. Practical testing and iterative adjustments are key to overcoming these challenges.