Welcome to RL-Devlopments, a repository dedicated to the development and implementation of advanced reinforcement learning algorithms and methodologies. This project focuses on various aspects of reinforcement learning, including self-curing agents, curiosity-driven exploration, model-based reinforcement learning, and recent advancements in the field.
- Self-Curing Reinforcement Learning Agents: Agents that adapt and improve their performance over time.
- Curiosity-Driven Exploration: Techniques to enhance exploration strategies in RL.
- Model-Based Reinforcement Learning: Implementations of model-based approaches for improved learning efficiency.
- Advanced Algorithms: A collection of state-of-the-art reinforcement learning algorithms.
To install the required packages, create a virtual environment and use the requirements.txt file provided in the repository.
# Create a virtual environment
python -m venv rl-env
# Activate the virtual environment
# On Windows
rl-env\Scripts\activate
# On macOS/Linux
source rl-env/bin/activate
# Install dependencies
pip install -r requirements.txtHere are some quick examples to get you started:
from rl_module import RLEnvironment
from self_curing_rl import SelfCuringRLAgent
env = RLEnvironment("CartPole-v1")
agent = SelfCuringRLAgent(features=[64, 64], action_dim=env.action_space.n)
# Train the agent
training_info = agent.train(env, num_episodes=1000, max_steps=500)
print(f"Final reward: {training_info['final_reward']}")# Simulate performance degradation
agent.performance = 0.7
# Diagnose and heal
issues = agent.diagnose()
if issues:
print(f"Detected issues: {issues}")
agent.heal(env, num_episodes=500, max_steps=500)
print(f"Healing completed. New performance: {agent.performance}")Contributions are welcome! Please see CONTRIBUTING.md for guidelines on how to contribute to the project.
This project is licensed under the MIT License. See the LICENSE file for more details.
For questions, suggestions, or feedback, feel free to open an issue on the GitHub repository.