Self-Preserving Genetic Algorithms vs. Safe Reinforcement Learning in Discrete Action Spaces

Safe learning techniques are learning frameworks that take safety into consideration during the training process. Safe reinforcement learning (SRL) combines reinforcement learning (RL) with safety mechanisms such as action masking and run time assurance to protect an agent during the exploration of its environment. This protection, though, can severely hinder an agent's ability to learn optimal policies as the safety systems exacerbate an already difficult exploration challenge for RL agents. An alternative to RL is an optimization approach known as genetic algorithms (GA), which utilize operators that mimic biological evolution to evolve better policies. By combining safety mechanisms with genetic algorithms, this work demonstrates a novel approach to safe learning called Self-Preserving Genetic Algorithms. To highlight the training benefits of SPGA compared to SRL in discrete action spaces, this demonstration trains and deploys an SPGA agent with action masking (SPGA-AM) and an SRL agent with action masking (SRL-AM) in real-time in the CartPole-v0 environment with a safety boundary condition b = 0.75. After training, each of the learned policies are tested in a CartPole-v0 environment with an extended max timesteps value (T = 200 -> T = 1000). After the demo, users will have a better understanding of SPGA and SRL training, as well as the benefits of using SPGA to train in discrete action spaces.