A simple exercise in reinforcement learning
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Updated
Mar 24, 2022 - Jupyter Notebook
A simple exercise in reinforcement learning
Active versus Passive exploration
OSPO is a novel metaheuristic algorithm which has the potential to solve different kinds of problems with promising performance.
Maintain an environmental exploration map & Update by Bayesian probability **For Autonomous Vehicle**
OpenAI, gym environment implementation
A companion repository for 'Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task'
Exploitation vs Exploration problem stated as A/B-testing with maximum profit per unit time.
An Optimistic Approach to the Q-Network Error in Actor-Critic Methods
over-parameterization = exploration ?
Repository for our paper: "Improving Reinforcement Learning Exploration with Causal Models of Core Environment Dynamics". (submitted to ECAI 2024)
Human and sim. behavioral / small-scale neural data for paper: https://www.biorxiv.org/content/10.1101/2022.10.03.510668v2
This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.
This project uses Reinforcement Learning to teach an agent to drive by itself and learn from its observations so that it can maximize the reward(180+ lines)
Action elimination for multi-armed bandits
Reinforcement Learning (COMP 579) Project
This repository contains a variety of projects related to reinforcement learning, showcasing different approaches to implementing it in various scenarios.
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