A continuous action space version of A3C LSTM in pytorch plus A3G design
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Updated
Oct 11, 2024 - Python
A continuous action space version of A3C LSTM in pytorch plus A3G design
Train an RL agent to execute natural language instructions in a 3D Environment (PyTorch)
Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games
This project explores deep reinforcement learning, hybrid actor-critic approach with A3C/PPO combined with curiosity for playing Super Mario Bros
Reinforcement Learning for Super Mario Bros using A3C on GPU
World Models with A3C on Carracing-v0 in gym
Deep Reinforcement Learning algorithms for Policy Value methods written from scratch.
PyTorch implementation of two variants of the Harlow visual fixation task (PsychLab and 1D version). Reproduces the results found in two papers. Check the ReadMe for more details!
Attentional Mechanism incorporated in Asynchronous Advantage Actor Critic a3c/a2c deep mind
Asynchronous Advantage Actor-Critic using Generalized Advantage Estimation (PyTorch)
Asynchronous Advantage Actor-Critic (A3C+LSTM) for Sonic the Hedgehog using Tensorflow 2
PyTorch implementation of A3C (Asynchronous Advantage Actor Critic)
Research Project in A3C reinforcement learning algorithm used for path finding mobile robots
Deep-Reinforcement-Learning with A3C in 'Super Mario Bros' (NES, 1985)
Implementation of the World's most Advance AI Algorithm (Jan 2024) A3C created by GoogleDeepMind combining DQN with Deep Policy Network, uses multiple agents ot learn faster and reduce training time.
Implementation of Meta-RL A3C algorithm
This repository contains high quality and tested implementation of Asynchronous Actor Critic Algorithm
Implement A3C to Train KungFu Master
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