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PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.

Policy Gradient is all you need! A step-by-step tutorial for well-known PG methods.

Implementation of Algorithms from the Policy Gradient Family. Currently includes: A2C, A3C, DDPG, TD3, SAC

Repository for the PGA-MAP-Elites algorithm. PGA-MAP-Elites was developed to efficiently scale MAP-Elites to large genotypes and noisy domains. It uses Neuroevolution driven by a Genetic Algorithm (GA) coupled with Policy Gradients (PG) derived from an off-policy Deep Reinforcement Learning met... Read more

Photo Optimizing Adversarial Net with Policy Gradient Method

Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. This paper primarily focuses on ... Read more

This Reinforcement learning agent uses Policy-Gradient method to trade the market

[ICLR 2026] PSFT is a trust-region–inspired fine-tuning objective that views SFT as a policy gradient method with constant advantages, constraining policy drift to stabilize training and improve generalization.

Code for the paper: "Fine-Tuning Discrete Diffusion Models with Policy Gradient Methods"

Tensorflow implement of paper: Optimization of image description metrics using policy gradient methods