Simple_DDQN_Cartpole: Applying Double Deep Q-Networks to train an agent for balancing a pole on a cart. Reinforcement learning project focuses on efficient learning and improved decision-making for mastering the Cartpole control problem.
agent.py
: Contains classes for the DDQN agent and its training algorithm.train.py
: Initializes the Cartpole environment and trains the agent. It saves model weights and displays training curves.test.py
: Uses saved model weights to test the agent's inference performance.
- Install the necessary dependencies: Python 3.x, PyTorch, NumPy.
- Run
train.py
to train the DDQN agent on the Cartpole environment. - After training, model weights are saved for future use.
- Run
test.py
to evaluate the trained agent's performance using the saved weights. - Training progress and inference results can be monitored through the displayed curves and outputs.