RLXtreme is a powerful and efficient Python package for reinforcement learning algorithms, designed to provide state-of-the-art performance and tackle challenging problems. It integrates a wide range of classical and modern reinforcement learning algorithms, including Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and more, to meet the demands of various tasks and applications.
- High-performance reinforcement learning algorithms
- Efficient training and decision-making processes
- Support for popular algorithms such as (C)MAB, DQN, PPO, SAC, etc.
- Advanced optimization techniques for scalability
- Tools for data collection, model evaluation, and result visualization
- User-friendly API for easy model development and experimentation
- Distributed computing and parallel training capabilities
To install RLXtreme, you can use pip:
pip install.
Here's a simple example that demonstrates how to train a DQN agent using RLXtreme:
import rlxtreme
# Create environment
env = rlxtreme.make_env("CartPole-v1")
# Create DQN agent
agent = rlxtreme.DQN()
# Train the agent
agent.train(env)
# Evaluate the trained agent
rewards = agent.evaluate(env)
print("Average reward:", sum(rewards) / len(rewards))