ELE2364 (reinforcement learning) support package

Package to support the deep reinforcement learning exercises of the ELE2364 (reinforcement learning) course at PUC-Rio.

Copyright (c) 2024 Wouter Caarls Parts copyright (c) 2020 Gabriel Nogueira (Talendar)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Introduction

This package provides the environments and approximators used in the ELE2364 (reinforcement learning) course at PUC-Rio.

To install, run

´´´ pip install ele2364 ´´´

Environments

The environments provided in ele2364.environments are:

• Pendulum (Pendulum-v1 from Gymnasium)
• Lander (LunarLander-v2 from Gymnasium)
• FlappyBird (FlappyBird-v0 by Gabriel Nogueira (Talendar))

Networks

The networks provided in ele2364.networks are:

• V (state-value network)
• DQ (state-action value network with discrete actions)
• CQ (state-action value network with continuous actions)
• Mu (deterministic policy network)
• Pi (stochastic policy network)