ivy-gym 1.1.5

Fully differentiable reinforcement learning environments, written in Ivy.

What is Ivy Gym?

Ivy Gym opens the door for intersectional research between supervised learning (SL), reinforcement learning (RL), and trajectory optimization (TO), by implementing RL environments in a fully differentiable manner.

Specifically, Ivy gym provides differentiable implementations of the classic control tasks from OpenAI Gym, as well
as a new Swimmer task, which illustrates the simplicity of creating new tasks using Ivy. The differentiable nature
of the environments means that the cumulative reward can be directly optimized for in a supervised manner, without
need for reinforcement learning, which is the de facto approach for optimizing cumulative rewards. Ivy currently
supports Jax, TensorFlow, PyTorch, MXNet and Numpy. Check out the [docs](https://ivy-dl.org/gym) for more info!