This repository has all the codes and sources of various RL algorithms that I have implemented.
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Updated
Sep 22, 2020 - Python
This repository has all the codes and sources of various RL algorithms that I have implemented.
A collection of deep and tabular reinforcement learning algorithms, including implementations of DQN, Dueling DQN, Q-learning, SARSA, n-step Tree Backup, and Monte Carlo.
Q-Learning Agent that Learns and Plays Alan Parr’s Traffic Lights Game
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Comparative study of Monte Carlo, SARSA & Q-Learning on custom MiniGrid environments. Phase-conditioned state representation, one-shot event-based reward shaping, Optuna hyperparameter search. TD methods: 100% success; MC: 61% on the hard env. RL course HW2, Reichman University.
Benchmarking 11 tabular RL methods on a 6×6 FrozenLake grid under deterministic and slippery dynamics.
Implementation of Q-Learning, SARSA, and Dyna Q to allow an agent to navigate the FrozenLake-v0 environment from OpenAI.
RL was cheaper. The heuristic was safer. Neither was correct. POLARIS stress-tests operational policies under chaos, demand spikes, and black swan events asking one question: which policy survives when everything goes wrong? Built with constrained RL, Bayesian modeling, CVaR risk metrics, and a human-in-the-loop governor.
Q-Learning agent that learns to navigate a 4×12 cliff-edge grid using ε-greedy exploration and Bellman updates — trained from scratch with NumPy + Gymnasium in 63 lines of Python.
Optimize RAN parameters across 50 cells via tabular Q-Learning on cell-level KPI states (+61% vs random)
Matchbox-RL: A tangible reinforcement learning library for Python. Based on Donald Michie's 1961 MENACE algorithm using matchboxes and colored beads. Perfect for education and visualizing RL.
强化学习中文学习笔记与可复现实验仓库,覆盖动态规划、表格方法、DQN、策略梯度、PPO、SAC 与安全强化学习专题。
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