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Snake game environment integrated with OpenAI Gym. Proximal Policy Optimization (PPO) implementation for training. Visualization of training progress and agent performance. Easy to understand code.
Developed an highly customizable OpenAI gym environment and trained a stable_baselines3 PPO agent. Used the expert agent for Imitation Learning with DAgger
This project is based on fine-tuning LLM models (FLAN-T5) for text summarisation task using PEFT approach. All evaluation metrics being computed on ROUGE scoring and LoRA optimisation techniques being used for fine-tuning.
The aim of this repository is the analysis and study of computer intelligence and in-depth learning techniques in the development of intelligent gaming agents.
This repository hosts a customized PPO based agent for Carla. The goal of this project is to make it easier to interact with and experiment in Carla with reinforcement learning based agents -- this, by wrapping Carla in a gym like environment that can handle custom reward functions, custom debug output, etc.
In this project, I have tried to use DeepRL for optimizing the selection of transactions done by the miner to increase the fee when they execute a block on the chain