lab6

Deep Q-Network and Deep Deterministic Policy Gradient

Implement DQN and DDQN to solve LunarLander-v2. Implement DDPG to solve LunarLanderContinuous-v2.

Data

The data comes from OpenAI Gym. You can get the data by installing in your conda environment:

pip install gym

Importing in your Python code:

import gym
env=gym.make('LunarLander-v2')

Training

Install dependencies

conda env create -f environment.yml
conda activate dqn

Sample running code

python dqn.py \
    -m dqn.pth \
    --test_only True \
    --seed 20200519 \
    --test_epsilon 0.001

Results

DQN

Training ewma reward	Testing results

DDPG

Training ewma reward	Testing results

DDQN

Training ewma reward	Testing results