diff --git a/_posts/2023-12-07-curiosity-es.md b/_posts/2023-12-07-curiosity-es.md new file mode 100644 index 0000000..99290b0 --- /dev/null +++ b/_posts/2023-12-07-curiosity-es.md @@ -0,0 +1,28 @@ +## Curiosity Creates Diversity in Policy Search + +We are pleased to announce the publication of the research paper titled "Curiosity Creates Diversity in Policy Search" in the journal Transactions on Evolutionary Learning and Optimization. +This work was co-authored by Paul-Antoine, Emmanuel, Yann Besse and Dennis. + +### Abstract of the Research +When searching for policies, reward-sparse environments often lack sufficient +information about which behaviors to improve upon or avoid. In such environments, +the policy search process is bound to blindly search for reward-yielding transitions +and no early reward can bias this search in one direction or another. A way to +overcome this is to use intrinsic motivation in order to explore new transitions +until a reward is found. In this work, we use a recently proposed definition of +intrinsic motivation, Curiosity, in an evolutionary policy search method. We +propose Curiosity-ES, an evolutionary strategy adapted to use Curiosity as a +fitness metric. We compare Curiosity-ES with other evolutionary algorithms +intended for exploration, as well as with Curiosity-based reinforcement learning, +and find that Curiosity-ES can generate higher diversity without the need for an +explicit diversity criterion and leads to more policies which find reward. + +### Key Findings + +* An algorithm that creates diversity without the need for an explicit diversity criterion. +* Curiosity-ES outperforms other exploration algorithms in sparse-reward scenarios. +* Empirical demonstration that combining the Curiosity exploration bonus with Evolutionary Strategies (ES) maintains a better balance in the inherent two-player game of exploration methods using uncertainty bonuses. +### Publication Details +* **Title**: Curiosity Creates Diversity in Policy Search +* **Journal**: Transactions on Evolutionary Learning and Optimization +* **Link to Publication**: Read the full paper [here](https://dl.acm.org/doi/abs/10.1145/3605782) \ No newline at end of file