Reinforcement Learning (RL) is a fascinating subset of machine learning that enables agents to learn optimal behaviors through trial and error. This article delves into practical implementations of popular RL algorithms using TensorFlow, particularly focusing on continuous domains, while providing insights for discrete scenarios as well.

Visualizing Success with BipedalWalker-v2 and CarRacing-v0

In the journey of RL, visual feedback is invaluable. Here are two exciting examples:

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These GIFs illustrate two challenges: BipedalWalker-v2 solved with DPPO utilizing an LSTM layer, and CarRacing-v0 tackled by PPO with a combined actor-critic network. It’s impressive to witness how different architectures can lead to significant variations in performance.

Implemented Algorithms

With gratitude toward DeepMind and OpenAI for their groundbreaking works, below is a list of notable algorithms implemented in this project:

Algorithm	Paper
DPPG	Continuous control with deep reinforcement learning
A3C	Asynchronous Methods for Deep Reinforcement Learning
PPO	Proximal Policy Optimization Algorithms
DPPO	Emergence of Locomotion Behaviours in Rich Environments
GAE	High-Dimensional Continuous Control Using Generalized Advantage Estimation

While GAE is integrated into most algorithms, it’s worth noting that DPPG does not utilize it. Additionally, an LSTM layer has been included in policy and value functions where feasible, leading to improved scores in several environments, albeit with some stability concerns.

How to Train Your Models

All Python scripts are designed as standalone and can be executed directly within your IDE. Alternatively, use the terminal’s -m flag:

rl-examples$ python3 -m ppo.ppo_joined

Each model and its corresponding TensorBoard summaries will be stored in the same directory as the script. For those using DPPO, a helper script is available to launch worker threads:

rl-examples$ sh dppostart_dppo.sh

Essential Requirements

Before diving into your reinforcement learning implementations, ensure that you have the following installed:

• Python 3.6+
• OpenAI Gym 0.10.3+
• TensorFlow 1.11
• Numpy 1.13+

It is crucial to note that DPPO was tested on a 16-core machine using CPU only, and some adjustments may be necessary based on your specific hardware setup. Interestingly, while BipedalWalker performed similarly on CPU versus GPU (GTX 1080), CarRacing benefited from using CNN layers.

Troubleshooting

As with any intricate system, you’re bound to encounter some hiccups. Here are a few ideas if you run into issues:

• Finding the right parameters for PPO in discrete action spaces for Atari is essential; consider consulting the relevant papers for insights.
• Ensure the LSTM batching in A3C is correctly implemented as there are common pitfalls in its application.
• If you are experiencing instability with distributed PPO using LSTM, try lowering the learning rates.

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Conclusion

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.