Dead-simple self-learning offers developers a dead-simple approach to create and train reinforcement learning agents in Python. The framework abstracts core RL components, such as environment wrappers, policy modules, and experience buffers, into concise interfaces. Users can quickly initialize environments, define custom policies using familiar PyTorch or TensorFlow backends, and execute training loops with built-in logging and checkpointing. The library supports on-policy and off-policy algorithms, enabling flexible experimentation with Q-learning, policy gradients, and actor-critic methods. By reducing boilerplate code, dead-simple self-learning allows practitioners, educators, and researchers to prototype algorithms, test hypotheses, and visualize agent performance with minimal configuration. Its modular design also facilitates integration with existing ML stacks and custom environments.

This repository provides an end-to-end reinforcement learning framework for StarCraft II gameplay research. The core agent uses Proximal Policy Optimization (PPO) to learn policy networks that interpret observation data from the PySC2 environment and output precise in-game actions. Developers can configure neural network layers, reward shaping, and training schedules to optimize performance. The system supports multiprocessing for efficient sample collection, logging utilities for monitoring training curves, and evaluation scripts for running trained policies against scripted or built-in AI opponents. The codebase is written in Python and leverages TensorFlow for model definition and optimization. Users can extend components such as custom reward functions, state preprocessing, or network architectures to suit specific research objectives.

TexasHoldemAgent provides a modular environment built on Python to train, evaluate, and deploy an AI-powered poker player for heads-up limit Texas Hold’em. It integrates a custom simulation engine with deep reinforcement learning algorithms, including DQN, for iterative policy improvement. Key capabilities include hand state encoding, action space definition (fold, call, raise), reward shaping, and real-time decision evaluation. Users can customize learning parameters, leverage CPU/GPU acceleration, monitor training progress, and load or save trained models. The framework supports batch simulation to test various strategies, generate performance metrics, and visualize win rates, empowering researchers, developers, and poker enthusiasts to experiment with AI-driven gameplay strategies.

Text-to-Reward provides a pipeline to train reward models that map text-based task descriptions or feedback into scalar reward values for RL agents. Leveraging transformer-based architectures and fine-tuning on collected human preference data, the framework automatically learns to interpret natural language instructions as reward signals. Users can define arbitrary tasks via text prompts, train the model, and then incorporate the learned reward function into any RL algorithm. This approach eliminates manual reward shaping, boosts sample efficiency, and enables agents to follow complex multi-step instructions in simulated or real-world environments.

uAgents is a modular JavaScript framework that empowers developers to build autonomous, decentralized AI agents which can discover peers, exchange messages, collaborate on tasks, and adapt through learning. Agents communicate over libp2p-based gossip protocols, register capabilities via on-chain registries, and negotiate service-level agreements using smart contracts. The core library handles agent lifecycle events, message routing, and extensible behaviors such as reinforcement learning and market-driven task allocation. Through customizable plugins, uAgents can integrate with Fetch.ai’s ledger, external APIs, and oracle networks, enabling agents to perform real-world actions, data acquisition, and decision-making in distributed environments without centralized orchestration.

Vanilla Agents is a lightweight PyTorch-based framework that delivers modular and extensible implementations of core reinforcement learning agents. It supports algorithms like DQN, Double DQN, PPO, and A2C, with pluggable environment wrappers compatible with OpenAI Gym. Users can configure hyperparameters, log training metrics, save checkpoints, and visualize learning curves. The codebase is organized for clarity, making it ideal for research prototyping, educational use, and benchmarking new ideas in RL.

VMAS is a comprehensive toolkit for building and training multi-agent systems using deep reinforcement learning. It supports GPU-based parallel simulation of hundreds of environment instances, enabling high-throughput data collection and scalable training. VMAS includes implementations of popular MARL algorithms like PPO, MADDPG, QMIX, and COMA, along with modular policy and environment interfaces for rapid prototyping. The framework facilitates centralized training with decentralized execution (CTDE), offers customizable reward shaping, observation spaces, and callback hooks for logging and visualization. With its modular design, VMAS seamlessly integrates with PyTorch models and external environments, making it ideal for research in cooperative, competitive, and mixed-motive tasks across robotics, traffic control, resource allocation, and game AI scenarios.

The YGO-Agent framework allows researchers and enthusiasts to develop AI bots that play the Yu-Gi-Oh card game using reinforcement learning. It wraps the YGOPRO game simulator into an OpenAI Gym-compatible environment, defining state representations such as hand, field, and life points, and action representations including summoning, spell/trap activation, and attacking. Rewards are based on win/loss outcomes, damage dealt, and game progress. The agent architecture uses PyTorch to implement DQN, with options for custom network architectures, experience replay, and epsilon-greedy exploration. Logging modules record training curves, win rates, and detailed move logs for analysis. The framework is modular, enabling users to replace or extend components such as the reward function or action space.

GYM_XPLANE_ML wraps the X-Plane flight simulator as an OpenAI Gym environment, exposing throttle, elevator, aileron and rudder controls as action spaces and flight parameters like altitude, speed, and orientation as observations. Users can script training workflows in Python, select predefined scenarios or customize waypoints, weather conditions, and aircraft models. The library handles low-latency communication with X-Plane, runs episodes in synchronous mode, logs performance metrics, and supports real-time rendering for debugging. It enables iterative development of ML-driven autopilots and experimental RL algorithms in a high-fidelity flight environment.

AI-Agentic Machine Translation is an open-source framework designed for research and development in machine translation. It orchestrates three core agents—a generator, an evaluator, and a refiner—to collaboratively produce, assess, and refine translations. Built on PyTorch and transformer models, the system supports supervised pre-training, reinforcement learning optimization, and configurable agent policies. Users can benchmark on standard datasets, track BLEU scores, and extend the pipeline with custom agents or reward functions to explore agentic collaboration in translation tasks.

AI Hedge Fund 5zu provides a complete pipeline for quantitative trading: a customizable environment for simulating multiple asset classes, reinforcement learning–based agent modules, backtesting utilities, real-time market data integration, and risk management tools. Users can configure data sources, define reward functions, train agents on historical data, and evaluate performance across key financial metrics. The framework supports modular strategy development and can be extended to live broker APIs for deploying production-level trading bots.

AI Agents for Rock Paper Scissors is an open-source Python project that demonstrates how to build, train, and evaluate different AI strategies—random play, rule-based pattern recognition, and reinforcement learning (Q-learning)—in the classic Rock-Paper-Scissors game. It provides modular agent classes, a configurable game runner, performance logging, and visualization utilities. Users can easily swap agents, adjust learning parameters, and explore AI behavior in competitive scenarios.

The Beer Game Environment provides a discrete-time simulation of a four-stage beer supply chain—retailer, wholesaler, distributor, and manufacturer—exposing an OpenAI Gym interface. Agents receive observations including on-hand inventory, pipeline stock, and incoming orders, then output order quantities. The environment computes per-step costs for inventory holding and backorders, and supports customizable demand distributions and lead times. It integrates seamlessly with popular RL libraries like Stable Baselines3, enabling researchers and educators to benchmark and train algorithms on supply chain optimization tasks.

BotPlayers is a versatile open-source framework designed to streamline the development and deployment of AI-driven game-playing agents. It features a flexible environment abstraction layer that supports screen scraping, web APIs, or custom simulation interfaces, allowing bots to interact with various games. The framework includes built-in reinforcement learning algorithms, genetic algorithms, and rule-based heuristics, along with tools for data logging, model checkpointing, and performance visualization. Its modular plugin system enables developers to customize sensors, actions, and AI policies in Python or Java. BotPlayers also offers YAML-based configuration for rapid prototyping and automated pipelines for training and evaluation. With cross-platform support on Windows, Linux, and macOS, this framework accelerates experimentation and production of intelligent game agents.

CityLearn provides a modular simulation platform for energy management research using reinforcement learning. Users can define multi-zone building clusters, configure HVAC systems, storage units, and renewable sources, then train RL agents against demand response events. The environment exposes state observations like temperatures, load profiles, and energy prices, while actions control setpoints and storage dispatch. A flexible reward API allows custom metrics—such as cost savings or emission reductions—and logging utilities support performance analysis. CityLearn is ideal for benchmarking, curriculum learning, and developing novel control strategies in a reproducible research framework.

CryptoTrader Agents provides a comprehensive toolkit for designing, training, and deploying AI-driven trading strategies in cryptocurrency markets. It includes a modular environment for data ingestion, feature engineering, and custom reward functions. Users can leverage preconfigured reinforcement learning algorithms or integrate their own models. The platform offers simulated backtesting on historical price data, risk management controls, and detailed metric tracking. When ready, agents can connect to live exchange APIs for automated execution. Built on Python, the framework is fully extensible, enabling users to prototype new tactics, run parameter sweeps, and monitor performance in real time.

Fast Reinforcement Learning is a specialized Python framework designed to accelerate the development and execution of reinforcement learning agents. It offers out-of-the-box support for popular algorithms such as PPO, A2C, DDPG and SAC, combined with high-throughput vectorized environment management. Users can easily configure policy networks, customize training loops and leverage GPU acceleration for large-scale experiments. The library’s modular design ensures seamless integration with OpenAI Gym environments, enabling researchers and practitioners to prototype, benchmark and deploy agents across a variety of control, game and simulation tasks.

DeepSeek R1 represents a significant breakthrough in artificial intelligence, delivering top-tier performance in reasoning, mathematics, and coding tasks. Utilizing a sophisticated MoE (Mixture of Experts) architecture with 37B activated parameters and 671B total parameters, DeepSeek R1 implements advanced reinforcement learning techniques to achieve state-of-the-art benchmarks. The model offers robust performance, including 97.3% accuracy on MATH-500 and a 96.3% percentile ranking on Codeforces. Its open-source nature and cost-effective deployment options make it accessible for a wide range of applications.

Dino Reinforcement Learning offers a comprehensive toolkit for training an AI agent to play the Chrome dinosaur game via reinforcement learning. By integrating with a headless Chrome instance through Selenium, it captures real-time game frames and processes them into state representations optimized for deep Q-network inputs. The framework includes modules for replay memory, epsilon-greedy exploration, convolutional neural network models, and training loops with customizable hyperparameters. Users can monitor training progress via console logs and save checkpoints for later evaluation. Post-training, the agent can be deployed to play live games autonomously or benchmarked against different model architectures. The modular design allows easy substitution of RL algorithms, making it a flexible platform for experimentation.

DQN-Deep-Q-Network-Atari-Breakout-TensorFlow provides a complete implementation of the DQN algorithm tailored for the Atari Breakout environment. It uses a convolutional neural network to approximate Q-values, applies experience replay to break correlations between sequential observations, and employs a periodically updated target network to stabilize training. The agent follows an epsilon-greedy policy for exploration and can be trained from scratch on raw pixel input. The repository includes configuration files, training scripts to monitor reward growth over episodes, evaluation scripts to test trained models, and TensorBoard utilities for visualizing training metrics. Users can adjust hyperparameters such as learning rate, replay buffer size, and batch size to experiment with different setups.