Clean and flexible implementation of PPO (built on top of stable-baselines3)
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Updated
Jul 9, 2021 - Python
Clean and flexible implementation of PPO (built on top of stable-baselines3)
Modular Deep RL infrastructure in PyTorch
Implement PPO to solve Crawler problem in Unity
An implementation from the state-of-the-art family of reinforcement learning algorithms Proximal Policy Optimization using normalized Generalized Advantage Estimation and optional batch mode training. The loss function incorporates an entropy bonus.
A pytorch project to easily run experiments on OpenAI's Procgen Benchmark
An implementation of Proximal Policy Optimization using TensorFlow. Tested on the OpenAI Gym car racing environment.
Training PPO agents in OpenAI Gym and PyBullet environments.
A demonstration of some prominent reinforcement learning algorithms
Single file implementation of Deep Reinforcement Learning algorithm (PPO) based on LunarLander-v2 environment
Custom Reinforcement Learning Agents
Hybrid Transformer based Multi-agent Reinforcement Learning (HTransRL) is for drone coordination in air corridors, addressing the challenges of dynamic dimensions and types of state inputs, which cannot addressed by the traditional MARL.
Implementation of PPO with TF 2.0 and Pyoneer.
The CAT Optimal Hybrid Solver is a tool designed to tackle the cross array task (CAT) activity designed to assess algorithmic thinking skills in the context of K-12 education.
Training a Reinforcement Learning Agent to Play Flappy Bird.
Implementations of deep reinforcement learning algorithms.
JAX Implementation of Proximal Policy Optimisation Algorithm
Gymnasium car environment. Autonomous Racing with Proximal Policy Optimization and custom tracks.
Nabi Deep Reinforcement Learning with PPO
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