Popular GitHub Repositories for Ddqn Per

Massively Parallel Deep Reinforcement Learning. ?

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

Minimal Deep Q Learning (DQN & DDQN) implementations in Keras

[ICLR 2025] SOTA discrete acoustic codec models with 40/75 tokens per second for audio language modeling

A library created to revitalize C++ as a machine learning front end. Per aspera ad astra.

? Enterprise-grade API gateway that helps you monitor and impose cost or rate limits per API key. Get fine-grained access control and monitoring per user, application, or environment. Supports OpenAI, Azure OpenAI, Anthropic, vLLM, and open-source LLMs.

Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent

CNN-based audio segmentation toolkit. Allows to detect speech, music, noise and speaker gender. Has been designed for large scale gender equality studies based on speech time per gender.

ChatGPT PROMPTs Splitter. Tool for safely process chunks of up to 15,000 characters per request

Keras Implementation of popular Deep RL Algorithms (A3C, DDQN, DDPG, Dueling DDQN)

PyTorch implementation of deep reinforcement learning algorithms

The full collection of all codes for my Youtube Channel segregated as per topic.

Trading Environment(OpenAI Gym) + DDQN (Keras-RL)

PyTorch per step fault tolerance (actively under development)

Anomaly detection analysis and labeling tool, specifically for multiple time series (one time series per category)

Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51

Personalized Federated Learning with Moreau Envelopes (pFedMe) using Pytorch (NeurIPS 2020)

PyTorch implementation of Soft-Actor-Critic and Prioritized Experience Replay (PER) + Emphasizing Recent Experience (ERE) + Munchausen RL + D2RL and parallel Environments.

A Real-Time-Strategy game for Deep Learning research

A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning (DRL) for Mobile Edge Computing (MEC) | This algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks.