Pytorch implementation of intrinsic curiosity module with proximal policy optimization
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Updated
Dec 20, 2018 - Python
Pytorch implementation of intrinsic curiosity module with proximal policy optimization
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)
Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning
Implementations for RL agents that seek to learn about their environment by predicting multiple signals from a single stream of experience.
Collection of Deep Reinforcement Learning Algorithms implemented in PyTorch.
Implemented DQN with Intrinsic Curiosity Module for a VizDoom competition at nate.
🎮 [IJCAI'20][ICLR'19 Workshop] Flow-based Intrinsic Curiosity Module. Playing SuperMario with RL agent and FICM!
Master's thesis on model-based intrinsically motivated reinforcement learning in robotic control
Pytorch based library containing reinforcement learning agents with forward models and intrinsic motivation modules
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM) on Pyramid env, Unity ML
A collection of my implemented advanced & complex RL agents for games like Soccer, Street Fighter, Mortal Kombat, Rubik's Cube, Vizdoom, Montezuma, Kungfu-master, Super-Mario-bros, HalfCheetah and more by implementing advanced DRL concepts like decision transformers, RND, MARL, A3C, ICM & sample_factory. To see my other rl agents please visit
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