Surprise your colleagues working on DQN by learning to land on moon in under 4 minutes using only 32 weights.
Run land.py to land on Moon!
Run sga_train.py to train a new agent.
See notes if having problems with PyBox2D.
Gradient Monte Carlo, Semi Gradient and Episodic Semi Gradient agents can be found in agent.py.
A GMC [1] agent implementation for solving Lunar Landing task.
Gradient Monte Carlo algorithm works on the basis of SGD, sampling states and rewards from an environment using provided policy and updating a differentiable value-approximation function at the end of sequence.
Weight update is performed separately for each state-action tuple, rather than accumulating the gradient over all sequence.
If having problems installing PyBox2D using pip, download the wheel from here
and install the package using pip install [wheel filename].
- "Sutton, R. S., Barto, A. G. (20181019). Reinforcement Learning: An Introduction."