RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
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Updated
May 14, 2024 - Python
RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
A platform for executing RRT exploration in ROS Noetic and Ubuntu 20.04LTS
A platform for executing RRT exploration in ROS Melodic and Ubuntu 18.04LTS
PyTorch implementation of Never Give Up: Learning Directed Exploration Strategies
Attention-based Curiosity-driven Exploration in Deep Reinforcement Learning
Motion planning and environment exploration with Bitcraze Crazyflie drones.
Repository for coExplore: Combining multiple rankings for multi-robot exploration
An open source reinforcement learning codebase with a variety of intrinsic exploration methods implemented in PyTorch.
Deep Recurrent Q-Network with different exploration strategies for self-driving cars (using AirSim)
code for reproducing results in paper on skill based exploration
Image processing using sequenced programming and turtle functions.
Implementation of Reinforcement Learning in Fall 2018
Exploration methods in Deep Reinforcement Learning.
Reference implementation of the Parameterized Indexed Networks
Trying to apply Deep RL + Geometric DL to graphs exploration
rrt/bfs/path_transform exploration algo for autonomous vehicle.
This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.
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