Contents
How do you evaluate the reinforcement learning model?
One way to show the performance of a reinforcement learning algorithm is to plot the cumulative reward (the sum of all rewards received so far) as a function of the number of steps. One algorithm dominates another if its plot is consistently above the other.
When should I stop RL training?
We generally stop the training of a deep learning model by looking at when the validation accuracy is least so as to prevent overfitting of the model. This is usually the premise of early stopping.
What is episode Q0?
For agents with a critic, Episode Q0 is the estimate of the discounted long-term reward at the start of each episode, given the initial observation of the environment. As training progresses, if the critic is well designed. Episode Q0 approaches the true discounted long-term reward, as shown in the preceding figure.
How is the environment related to the RL algorithm?
Then environment refers to the object that the agent is acting on (e.g. the game itself in the Atari game), while the agent represents the RL algorithm. The environment starts by sending a state to the agent, which then based on its knowledge to take an action in response to that state.
What are some tips and tricks for implementing RL?
Tips and Tricks when implementing an RL algorithm¶ When you try to reproduce a RL paper by implementing the algorithm, the nuts and bolts of RL research by John Schulman are quite useful . We recommend following those steps to have a working RL algorithm: Read the original paper several times; Read existing implementations (if available)
How are reinforcement learning algorithms used in games?
It was mostly used in games (e.g. Atari, Mario), with performance on par with or even exceeding humans. Recently, as the algorithm evolves with the combination of Neural Networks, it is capable of solving more complex tasks, such as the pendulum problem:
How is Gaussian distribution used in reinforcement learning?
Most reinforcement learning algorithms rely on a Gaussian distribution (initially centered at 0 with std 1) for continuous actions. So, if you forget to normalize the action space when using a custom environment, this can harm learning and be difficult to debug (cf attached image and issue #473 ).