Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).
-
Updated
Apr 17, 2024 - Python
Multi-Agent Constrained Policy Optimisation (MACPO; MAPPO-L).
Implementation of a Deep Reinforcement Learning algorithm, Proximal Policy Optimization (SOTA), on a continuous action space openai gym (Box2D/Car Racing v0)
Policy Optimization with Penalized Point Probability Distance: an Alternative to Proximal Policy Optimization
Mirror Descent Policy Optimization
[AAAI 2026] D²PPO: Diffusion Policy Policy Optimization with Dispersive Loss.
Codebase to fully reproduce the results of "No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO" (Moalla et al. 2024). Uses TorchRL and provides extensive tools for studying representation dynamics in policy optimization.
Model-based Policy Gradients
Code for Paper (Policy Optimization in RLHF: The Impact of Out-of-preference Data)
A modular, production-grade framework for Reinforcement Learning from Human Feedback (RLHF) with Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO).
Bittensor Subnet 11 — an open skill factory that uses distributed compute and RL to produce state-of-the-art skills for AI agents.
This repository contains the code for the paper "Local policy search with Bayesian optimization".
CPPO: Contrastive Perception Policy Optimization for VLM Agents
Implementation and explorations into MPO / DMPO
Reinforcement Learning (RL)! This repository is your hands-on guide to implementing RL algorithms, from Markov Decision Processes (MDPs) to advanced methods like PPO and DDPG. Build smart agents, learn the math behind policies, and experiment with real-world applications!
Paragraph-level Policy Optimization for Vision-Language Deepfake Detection - ICML 2026
Code for Policy Optimization as Online Learning with Mediator Feedback
An implementation of the reinforcement learning for CartPole-v0 by policy optimization
Autonomous reinforcement learning loop with adaptive hyperparameter tuning, multi-policy ensemble management, and framework-agnostic agent support. Self-converging via exponential backoff. Works with TensorFlow, PyTorch, JAX - bring any algorithm.
Code accompanying the NeurIPS 2025 paper "Sequential Monte Carlo for Policy Optimization in Continuous POMDPs".
Add a description, image, and links to the policy-optimization topic page so that developers can more easily learn about it.
To associate your repository with the policy-optimization topic, visit your repo's landing page and select "manage topics."