Black Box Meta-Learning Intrinsic Rewards

The broader application of reinforcement learning (RL) is limited by challenges including data efficiency, generalization capability, and ability to learn in sparse-reward environments. Meta-learning has emerged as a promising approach to address these issues by optimizing components of the learning algorithm to meet desired characteristics. Additionally, a different line of work has extensively studied the use of intrinsic rewards to enhance the exploration capabilities of algorithms. This work investigates how meta-learning can improve the training signal received by RL agents. We introduce a method to learn intrinsic rewards within a reinforcement learning framework that bypasses the typical computation of meta-gradients through an optimization process by treating policy updates as black boxes. We validate our approach against training with extrinsic rewards, demonstrating its effectiveness, and additionally compare it to the use of a meta-learned advantage function. Experiments are carried out on distributions of continuous control tasks with both parametric and non-parametric variations. Furthermore, only sparse rewards are used during evaluation. Code is available at: https: //github.com/Octavio-Pappalardo/Meta-learning-rewards