Custom Environments for training RL Agents
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May 30, 2020 - Python
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Custom Environments for training RL Agents
A collection of Single/Multiplayer Games Played by AI
Reinforcement Learning algorithms
A repo of papers I have read or will read
Evaluations and Datasets for Social Offline Reinforcement Learning
A path to deep learning skills
Analyzing Reinforcement Learning Benchmarks with Random Weight Guessing
An application of Reinforcement Learning (RL) from the SDD RL class to function approximators and deep learning.
Official repository for my MSc thesis: "Addressing Goal Misgeneralization with Natural Language Interfaces."
Reinforcement-learning agents trained to solve classic games.
CartPole-CrossEntropyMethod
Advantage Actor-Critic (A2C) reinforcement learning algorithm to detect emerging trends in tweets. The RL agent learns to optimize actions (post, edit, delete) based on engagement metrics such as likes, retweets, and quotes.
This repository contains code to train and test policies for a MPE environment (Simple Spread). Training is done using DQL for independent learning. Testing was done using 3 different policies: RL, Simple Policy, Complex Policy.
Constraint‑Aware Meta‑Optimizer for Policy‑Gradient RL
PPO vs Dueling Double DQN for MiniGrid MultiRoom navigation — curriculum learning across 2/4/6-room environments with custom reward shaping (Reichman University, 2025)
Reinforcement Learning : Autonomous parallel parking task. implementing SAC and DreamerV3's World Model on Highway-env
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