Deep Deterministic Policy Gradients

Implementation of the DDPG algorithm using tensorflow.keras and training agents on several environments.

The DDPG algorithm is based on the DQN algorithm and DPG algorithm, where an agent plays in continuous-based-action environments, and its model consists of two networks, an Actor network that is used to sample actions, and a Critic network that is used to compute the action-value Q.

Project structure

├── README.md
├── constants.py
├── ddpg.py
├── main.py
├── media/
├── memory.py
├── models.py
├── requirements.txt
├── trained_models/
├── utils.py
└── videos/

Usage

For training an agent on OpenAI gym environments, such as BipedalWalker-v3, run

python3 main.py --train --env_name=BipedalWalker-v3 --model_name=coolWalker --seed=2023

The agent models will be saved at trained_models/BipedalWalker-v3_2023_coolWalker.

For evaluating that agent run

python3 main.py --env_name=BipedalWalker-v3 --model_name=BipedalWalker-v3_2023_coolWalker --render_evaluation

For full list of command-line arguments and their descriptions run python3 main.py -h.

For training agents on MuJoCo environments, it needs to be installed manually first. See https://github.com/deepmind/mujoco.

Results

Agents on three different environments are trained. These are Lunar Lander, Bipedal Walker and Half Cheetah. Below are the training episodic returns. For the first two envs three agents are trained with different seeds and the graphs shows average return and the standard error is the shaded area of the runs. For HalfCheetah only one agent is trained.

Lunar Lander	Bipedal Walker	Half Cheetah

Videos

Lunar Lander	Bipedal Walker	Half Cheetah

Author

• trevis maldonado

Resources

Explanation of policy gradient RL and algorithms can be found at:

• https://lilianweng.github.io/posts/2018-04-08-policy-gradient/