  At present, robots typically require extensive training to successfully
accomplish a single task. However, to truly enhance their usefulness in
real-world scenarios, robots should possess the capability to perform multiple
tasks effectively. To address this need, various multi-task reinforcement
learning (RL) algorithms have been developed, including multi-task proximal
policy optimization (PPO), multi-task trust region policy optimization (TRPO),
and multi-task soft-actor critic (SAC). Nevertheless, these algorithms
demonstrate optimal performance only when operating within an environment or
observation space that exhibits a similar distribution. In reality, such
conditions are often not the norm, as robots may encounter scenarios or
observations that differ from those on which they were trained. Addressing this
challenge, algorithms like Q-Weighted Adversarial Learning (QWALE) attempt to
tackle the issue by training the base algorithm (generating prior data) solely
for a particular task, rendering it unsuitable for generalization across tasks.
So, the aim of this research project is to enable a robotic arm to successfully
execute seven distinct tasks within the Meta World environment. To achieve
this, a multi-task soft actor-critic (MT-SAC) is employed to train the robotic
arm. Subsequently, the trained model will serve as a source of prior data for
the single-life RL algorithm. The effectiveness of this MT-QWALE algorithm will
be assessed by conducting tests on various target positions (novel positions).
In the end, a comparison is provided between the trained MT-SAC and the
MT-QWALE algorithm where the MT-QWALE performs better. An ablation study
demonstrates that MT-QWALE successfully completes tasks with a slightly larger
number of steps even after hiding the final goal position.
