We present Brax, an open source library for rigid body simulation with a focus on performance and parallelism on accelerators, written in JAX.
To tackle this issue, we implement more than 50 of these choices in a generic adversarial imitation learning framework and investigate their impacts in a large-scale study (>500k trained agents) with both synthetic and human-generated demonstrations.
Empirically, agent-centric representation learning leads to the emergence of more complex cooperation strategies between agents as well as enhanced sample efficiency and generalization.
In recent years, reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
In recent years, on-policy reinforcement learning (RL) has been successfully applied to many different continuous control tasks.
Recent progress in the field of reinforcement learning has been accelerated by virtual learning environments such as video games, where novel algorithms and ideas can be quickly tested in a safe and reproducible manner.