In recent years, deep reinforcement learning (RL) has shown its effectiveness in solving complex continuous control tasks like locomotion and dexterous manipulation. However, this comes at the cost of an enormous amount of experience required for training, exacerbated by the sensitivity of learning efficiency and the policy performance to hyperparameter selection, which often requires numerous trials of time-consuming experiments. This work introduces a Population-Based Reinforcement Learning (PBRL) approach that exploits a GPU-accelerated physics simulator to enhance the exploration capabilities of RL by concurrently training multiple policies in parallel. The PBRL framework is applied to three state-of-the-art RL algorithms - PPO, SAC, and DDPG - dynamically adjusting hyperparameters based on the performance of learning agents. The experiments are performed on four challenging tasks in Isaac Gym - Anymal Terrain, Shadow Hand, Humanoid, Franka Nut Pick - by analyzing the effect of population size and mutation mechanisms for hyperparameters. The results demonstrate that PBRL agents outperform non-evolutionary baseline agents across tasks essential for humanoid robots, such as bipedal locomotion, manipulation, and grasping in unstructured environments. The trained agents are finally deployed in the real world for the Franka Nut Pick manipulation task. To our knowledge, this is the first sim-to-real attempt for successfully deploying PBRL agents on real hardware. Code and videos of the learned policies are available on our project website (https://sites.google.com/view/pbrl).

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