Reinforcement Learning Algorithms with Python

Advantages and disadvantages

When developing a reinforcement learning algorithm (all kinds of RL algorithms), there are three basic aspects to consider:

• Asymptotical performance: This is the maximum performance that an algorithm can achieve if it has infinite resources available in terms of both time and hardware.
• Wall clock time: This is the learning time required for an algorithm to reach a given performance with a given computational power.
• Sample efficiency: This is the number of interactions with the environment to reach a given performance.

We already explored sample efficiency in both model-free and model-based RL, and we saw how the latter is much more sample efficient. But what about wall clock time and performance? Well, model-based ...