TD3 for OpenAI gym environments using Tensorflow
To address the function approximation error of Actor-Critic methods, Fujimoto et al., proposed the Twin Delayed Deep Deterministic policy gradient method (TD3).
Using a total of six neural networks, TD3 minimises the approximated Q-value by taking the minimum value from two Critic neural networks and uses this value to optimise the Actor network.
The original implementation is written in Pytorch and is the basis for the Tensorflow version written here.
Requirements
Gym: pip install gym
Pybullet: pip install pybullet_envs
The below line in main.py has been commented out. To enable recordings of certain episodes simply uncomment line 19.
env = wrappers.Monitor(env, save_dir, force = True)
Note that one Windows machines ffmpeg will need to be saved in the same local directory as train.py.
Results
After 1 million timesteps the model will achieve an average total reward of 2200 for “AntBulletEnv-v0”.
The Actor loss has a local minimum around the -60 value where the agent will get stuck in a single position. Using Huber loss as opposed to MSE was instrumental in the agent progressing and achieving higher total rewards.
