Search Results for "actor"

...Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. ...

...Notifications are actually actions events, which are categorized by four main components. To generate a notification anywhere in your code, simply import the notify signal and send it with your actor, recipient, and verb. Generating notifications is probably best done in a separate signal. Using django-model-utils, we get the ability to add queryset methods to not only the manager, but to all querysets that will be used, including related objects. To ensure users always have the most up-to-date notifications, django-notifications includes a simple javascript API for updating specific fields within a django template. ...

...TRFL supports both CPU and GPU TensorFlow environments, though TensorFlow itself must be installed separately. It exposes clean, modular APIs for various RL methods including Q-learning, policy gradient, and actor-critic algorithms, among others. Each function returns not only the computed loss tensor but also a detailed structure containing auxiliary information like TD errors and targets.

MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. ...

A generic game engine built on top of libksd (http://libksd.sourceforge.net/) using the Atlas (http://www.worldforge.org/website/protocols/) for the network protocol.