🌍 Welcome to the Reinforcement Learning Practices repository! This repository is a collection of experiments, projects, and implementations focusing on various reinforcement learning (RL) algorithms applied to well-known RL problems. Whether you're a beginner or a researcher, you'll find a range of projects designed to demonstrate the strengths, weaknesses, and capabilities of different RL approaches.
- Lunar Lander RL Practices: Explore different RL techniques applied to the Lunar Lander problem, from DQN to D3QN and beyond.
- CartPole RL Practices: A collection of experiments focused on the classic Cart Pole problem using various RL algorithms.
The Lunar Lander environment challenges the agent to land a spaceship safely on a designated pad while minimizing fuel consumption and avoiding crashes. It’s a classic RL task used to test control policies. This project explores various RL approaches to solve this challenge.
- Deep Q-Networks (DQN): Combines Q-Learning with deep neural networks to learn optimal policies.
- Dueling Double DQN (D3QN): Adds improvements like Double Q-Learning and Dueling Networks to address overestimation and stabilize learning.
- Adaptive Gamma D3QN: Dynamically adjusts the discount factor ((\gamma)) during training to improve long-term reward learning.
- 🔄 Dynamic Gamma Adjustment for better reward optimization over time.
- 🏅 Dueling Networks that separate value from advantage estimations, improving stability and efficiency.
- 📊 Visualizations of training progress, including loss, reward, and Q-value plots.
Epoch 10 |
Epoch 500 |
Epoch 1000 |
Each subdirectory within the Lunar Lander project provides an in-depth implementation and visualization of the respective algorithms, showcasing how different methods tackle this complex RL task.
The CartPole problem is another classic reinforcement learning task where the agent’s goal is to balance a pole on a moving cart. This problem serves as a great testbed for exploring and evaluating various RL algorithms.
- Deep Q-Networks (DQN): A neural network-based method for approximating the Q-value function.
- Hyperparameter Exploration: Experiments with different learning rates, discount factors, and other hyperparameters to optimize performance.
- Boltzmann Exploration: Uses a probabilistic method to choose actions based on the Q-values, offering an alternative to epsilon-greedy exploration.
- SARSA (State-Action-Reward-State-Action): An on-policy algorithm that learns based on the action it actually took.
- 🧪 Experimentation with Hyperparameters: See how different settings impact learning efficiency and performance.
- 🎲 Boltzmann Exploration: A more probabilistic approach to action selection compared to traditional methods.
- 💻 Detailed Visualizations: See how different algorithms perform through visual aids like loss and reward plots.
Epoch 10 |
Epoch 500 |
Epoch 1000 |
Explore each subdirectory for the CartPole project to see the detailed implementations and performance analysis of various RL methods.
As we continue to explore and experiment with reinforcement learning algorithms, here are some exciting future directions for the repository:
- 🌟 Prioritized Experience Replay: Implementing a prioritized replay buffer to sample more important transitions during training, improving learning efficiency.
- 🌈 Rainbow DQN: Combining multiple improvements like Double DQN, Dueling Networks, Noisy Nets, and Prioritized Replay in a single framework for more efficient learning.
- ⚖️ Actor-Critic Methods: Exploring more advanced algorithms such as A3C, PPO, and DDPG, which can handle continuous action spaces, for more complex RL tasks.
We welcome contributions from the community! Whether you have suggestions, improvements, or new experiments to share, please feel free to submit a pull request or open an issue. Every contribution helps improve and expand the understanding of reinforcement learning in these exciting environments.
Happy exploring and learning! 🚀
Quote for Thought: “The beautiful thing about learning is that nobody can take it away from you.” — B.B. King