Reinforcement Learning Agents Implemented for Tensorflow 2.0+

Welcome to our comprehensive guide on leveraging Reinforcement Learning (RL) agents in Tensorflow 2.0+. In this article, we’ll take you through the latest updates, future plans, how to use these agents, and tips for hyperparameter tuning. Whether you’re a novice or an expert, we strive to make the installation and execution process seamless for everyone!

New Updates!

• DDPG with prioritized replay: This update ensures that experiences that are more valuable for learning are prioritized, thus improving the efficiency of the agent’s training.
• Primal-Dual DDPG for CMDP: This approach tackles Constrained Markov Decision Processes (CMDP), allowing for more nuanced learning in restricted environments.

Future Plans

We aim to introduce SAC Discrete which will allow for discrete action space support, providing more versatility in application.

Usage

To get started with implementing these agents, follow these steps:

• Install the dependencies using my tf2 conda env as reference.
• Each file in the repository contains example code that runs training on the CartPole environment.
• To train, simply run the command:

python3 TF2_DDPG_LSTM.py

• For monitoring the training process, utilize TensorBoard with the command:

tensorboard --logdir=DDPGlogs

Hyperparameter Tuning

To enhance the performance, you can fine-tune hyperparameters by following these steps:

• Install hyperopt from GitHub.
• You have the option to switch the agent used and configure the parameter space in hyperparam_tune.py.
• To run the tuning, execute:

python3 hyperparam_tune.py

Agents Overview

Below is a summary of agents tested using the CartPole environment:

Name	On/Off Policy	Model	Action Space Support
DQN	Off-policy	Dense, LSTM	Discrete
DDPG	Off-policy	Dense, LSTM	Discrete, Continuous
AE-DDPG	Off-policy	Dense	Discrete, Continuous
SAC	Off-policy	Dense	Continuous
PPO	On-policy	Dense	Discrete, Continuous

Constrained MDP Agents

For agents operating under constrained Markov Decision Processes, refer to the following:

Name	On/Off Policy	Model	Action Space Support
Primal-Dual DDPG	Off-policy	Dense	Discrete, Continuous

Visual Demos

We’ve included various demos showcasing the performance of different agents. Here are some of the recorded results:

• DQN Basic, 500 reward:
• DQN LSTM, 500 reward:
• DDPG Basic, 500 reward:
• DDPG LSTM, 500 reward:
• AE-DDPG Basic, 500 reward:
• PPO Basic, 500 reward:

Troubleshooting

If you encounter any issues during installation or execution, consider the following troubleshooting tips:

• Double-check that all dependencies are correctly installed and match the versions specified in the reference environment.
• If you’re having trouble running the training script, ensure that TensorFlow is properly installed and compatible with your environment.
• For detailed error messages, check your terminal output, which can provide insights into what’s going wrong.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.