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)
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Updated
Nov 25, 2022 - Jupyter Notebook
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)
A simple exercise in reinforcement learning
An Optimistic Approach to the Q-Network Error in Actor-Critic Methods
Active versus Passive exploration
over-parameterization = exploration ?
OSPO is a novel metaheuristic algorithm which has the potential to solve different kinds of problems with promising performance.
Reinforcement Learning (COMP 579) Project
Human and sim. behavioral / small-scale neural data for paper: https://www.biorxiv.org/content/10.1101/2022.10.03.510668v2
OpenAI, gym environment implementation
Repository Containing Comparison of two methods for dealing with Exploration-Exploitation dilemma for MultiArmed Bandits
This repository contains a variety of projects related to reinforcement learning, showcasing different approaches to implementing it in various scenarios.
Deep Intrinsically Motivated Exploration in Continuous Control
A companion repository for 'Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task'
Official implementation of LECO (NeurIPS'22)
This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.
This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.
Exploitation vs Exploration problem stated as A/B-testing with maximum profit per unit time.
Action elimination for multi-armed bandits
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