ROSARL: Reward-Only Safe Reinforcement Learning

Understand

An important problem in reinforcement learning is designing agents that learn to solve tasks safely in an environment.

• A common solution is for a human expert to define either a penalty in the reward function or a cost to be minimised when reaching unsafe states.
• However, this is non-trivial, since too small a penalty may lead to agents that reach unsafe states, while too large a penalty increases the time to convergence.
• Additionally, the difficulty in designing reward or cost functions can increase with the complexity of the problem.