Tensorflow implementation of Human-Level Control through Deep Reinforcement Learning
翻译 - 通过深度强化学习实现人为控制的Tensorflow实现
Lua/Torch implementation of DQN (Nature, 2015)
Vanilla DQN, Double DQN, and Dueling DQN implemented in PyTorch
Simple deep Q-learning agent.
翻译 - 简单的深度Q学习代理。
Basic DQN implementation
Reinforcement Learning for finance
Implementations of algorithms from the Q-learning family. Implementations inlcude: DQN, DDQN, Dueling DQN, PER+DQN, Noisy DQN, C51
DQN_play_sekiro
Deep Reinforcement Learning with DQN, Double DQN, Dueling DQN, Noisy Net (Noisy DQN), and DQN with Prioritized Experience Replay
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
Reinforcement Learning | tensorflow implementation of DQN, Dueling DQN and Double DQN performed on Atari Breakout
reinforcement learning, deep Q-network, double DQN, dueling DQN, prioritized experience replay
Implementation of Deep Reinforcement Learning Benchmark Algorithms, including DQN, Double DQN, Dueling DQN, Reinforce, Actor-Critic, A2C, A3C, etc.
DQN to play Atari Pong
[NeurIPS 2020, Spotlight] State-Adversarial DQN (SA-DQN) for robust deep reinforcement learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
Series Algorithms of Deep Reinforcement Learning, such as DQN, DDQN, one-step-DQN, DDPG, etc
Deep Reinforcement Learning codes for study. Currently, there are only codes for algorithms: DQN, C51, QR-DQN, IQN, QUOTA.
DQN by Matlab and Python
DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.
