Implementations of model-based Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
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Updated
Dec 4, 2017 - Python
Implementations of model-based Inverse Reinforcement Learning (IRL) algorithms in python/Tensorflow. Deep MaxEnt, MaxEnt, LPIRL
A simple Gridworld environment for Open AI gym
This repository provides a simulation of 4-Room-World environment.
Implementation of Reinforcement Algorithms from scratch
It is a java code which gives optimal policy for grid world problem in Artificial Intelligence.
Implementation of RL algorithms in various environments
Old and new Reinforcement Learning algorithms run on the GridUniverse ecosystem
Code for turning the FrozenLake env into its deterministic version
Using value iteration to find the optimum policy in a grid world environment.
Tabular methods for reinforcement learning
Simple gridworld environment for tabular reinforcement learning experiment
OpenAI gym-based algorithm for the grid world problem
SARSA and Q-Learning in Grid World
A CANDECOMP-PARAFAC tensor decomposition method to solve a Markov Decision Process (MDP) gridworld problem.
Creation of grid world environment through pygame package and optimizing the motion of agent through modified q-learning process. Video can be found here: https://www.youtube.com/watch?v=-nXH8k9gRLM
Reinforcement Learning experiments, comparing performance of Q-learning and Double Q-learning algorithms.
Compare the effect of a neutral market for RL Agents to trade shares or buy actions with/from others in different compositions.
Simple Minimalistic Gridworld Environment for OpenAI Gym (Simple-MiniGrid)
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