Lengthy evaluation times are common in many optimization problems such as policy learning tasks, especially when they involve the physical world. When evaluating solutions, it is sometimes clear that the objective function is not going to increase with additional computation time (for example when a two wheel robot continuously spins in place). In those cases, it makes sense to stop the evaluation early to save computation time. However, these approaches are problem specific and need to be specifically designed for the task at hand.

In our paper, we propose an early stopping method for policy learning. The proposed method only looks at the objective value at each time step and requires no problem specific knowledge. We tested the introduced stopping criterion in 6 policy learning environments and showed that it usually saves a significant amount of time. We also compare it with problem specific stopping criteria and show that it performs comparably, while being more general (it is not task specific).

Using the proposed stopping criterion does not require you to install anything. It is quite easy to integrate the proposed early stopping criterion in your project. Each time you evaluate a solution (e.g. a policy) $\theta$, you get an objective value $f[t](\theta)$ at each time step (e.g. in RL it would be the cumulative reward of $\theta$ at time step $t$). Then you stop evaluating $\theta$ at time step $t$ if:

$$t > t_{grace}$$

and

$$\max \lbrace f[t](\theta), f[t-t_{grace}](\theta) \rbrace < \min \lbrace f[t](\theta_{best}), f[t-t_{grace}](\theta_{best})\rbrace$$

where $t_{grace}$ is set to a fraction of the maximum episode length $t_{max}$. As a rule of thumb, we propose $t_{grace} = 0.2 \cdot t_{max}$. We assume a maximization setting: a higher value of $f$ is a better value.

Clone the repo and check the instructions to install in the folders inside other_RL/. This step can be omitted for generating the plots, but it is necessary to perform this step to launch the experiments. You also need to install the python dependencies specified in requirements.txt. The simulatedARE experiment has additional dependencies (see install_script_gecco21_leni_ARE.sh).

The experiments can be executed with the following four scripts:

• scripts/experiment_garage_CMAES_gym.py
• scripts/experiment_simulatedARE.py
• scripts/experiment_supermario.py
• scripts/experiment_veenstra.py

An extra parameter is required.

• --launch_local to execute the experiments where GESP is compared to problem specific approaches or to using no early stopping. Generates the data for all the experimental result figures up to and including Figure 12.
• --tgrace_different_values to execute the experiments that compare different t_grace parameter values. Generates the data for Figure 13 in the paper.
• --tgrace_nokill to execute the experiments that estimate the success rate for different t_grace parameter values. Generates the data for Figure 14 in the paper.
• --plot to generate the figures in the paper up to Figure 12, from the results in /results/data.

e.g. to generate the plots in the garage framework,

python scripts/experiment_garage_CMAES_gym.py --plot

To generate Figure 13 and Figure 14 (the figures of the t_grace experiments) for all of the environments, use

python scripts/utils/src_tgrace_experiment.py

The results obtained when launching the experiments with parameters --tgrace_different_values and --tgrace_nokill are required to generate these plots.

List of external resources used in the project:

• Coppelia Robotics
• Gym Rem2D by FrankVeenstra
• Autonomous Robotics Evolution by Leni Le Goff et al.
• Super Mario by Vivek Verma
• Gym Garage
• OpenAI Gym
• MuJoCo