q learning & deep q learning using pytorch
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Updated
Nov 15, 2019 - Python
q learning & deep q learning using pytorch
Reinforcement Learning Tutorials & other bedtime stories in PyTorch
A repo contains my implementation and analysis of some well-known Reinforcement Learning problems and algorithms.
Implementation and evaluation of the RL algorithm Rainbow to learn to play Atari games.
Implementing deep reinforcement learning algorithm for banana collector and other upcoming project. Using different technique such as Deep Q-network (DQN) and Double Deep Quick Network (DDQN)
DQN-Atari-Agents: Modularized & Parallel PyTorch implementation of several DQN Agents, i.a. DDQN, Dueling DQN, Noisy DQN, C51, Rainbow, and DRQN
Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.
A RL agent trained to play Mario using DDQN
Ensuring trust among agents using Multi-Agent Deep Reinforcement Learning
Deep reinforcement nano degree in Udacity - Prioritized Double DQN
Reinforcement Learning
In this repository, we try to solve musculoskeletal tasks with `Double DQN reinforcement learning` by using a `transformer` model has been used as the base model architecture.
⛷ DQN and DDQN algorithms for OpenAI Gym Skiing environment 🎮
This code is the result of the collaboration of RL Turkey team.
Reinforcement Learning Implementations in PyTorch
Implementation of a DDQN to play SMB for the NES.
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