Why Your RL Model Fails: Prioritized Replay and Actor-Critic in code (Part 2)

In Part 1 of our series on overcoming sparse rewards in reinforcement learning, we tackled the conceptual challenges. We explored the issues around sparse rewards, common pitfalls, and strategies to address them. In Part 2, we dive into the implementation details and bring those ideas to life through code.

The Code Breakdown

This video builds on the foundation laid earlier, where we discussed using an actor-critic architecture with a prioritized replay buffer. This approach helps the agent prioritize important experiences and accelerates learning in environments with sparse rewards, like the Squid Hunt game.

Here’s what’s new in this implementation:

• Custom Neural Network Architecture: We designed a flexible actor-critic model that integrates both value and policy learning using separate neural networks.
• Prioritized Replay Buffer: To avoid forgetting critical experiences, we implemented a replay buffer that prioritizes high-value transitions based on TD error.
• Enhanced Training Efficiency: We adjusted hyperparameters for learning rates, buffer size, and batch sizes specifically for the Squid Hunt environment, ensuring a balanced trade-off between exploration and exploitation.
• Multiple Epoch Training Loops: Unlike traditional reinforcement learning approaches that sample each transition once, our design loops through each episode for multiple epochs. This helps reinforce learning, allowing the agent to generalize better without losing important information.

Tracking Progress with WandB

One of the exciting additions in this part is the integration of Weights & Biases (WandB). Monitoring training metrics like rewards, losses, and model performance in real-time gives you a clear picture of how well your RL agent is doing. It also helps in quickly identifying issues like policy collapse, reward plateaus, or overfitting.

Key Challenges Addressed

• Dealing with Sparse Rewards: The custom reward shaping and prioritized replay buffer significantly improved the agent’s ability to navigate environments with infrequent rewards.
• Avoiding Forgetting: By replaying critical transitions multiple times during training, the agent retains knowledge longer and doesn’t immediately revert to suboptimal behavior.
• Improving Data Efficiency: Adjusting batch sizes and epochs per episode helped us achieve faster convergence while keeping the model from overfitting to specific episodes.

This video is packed with actionable insights and practical tips for anyone building RL models in complex environments. Watch till the end for a surprise performance boost tip that can make all the difference!

Don’t Miss Out! If you haven’t seen Part 1, check it out first to grasp the theoretical concepts that complement this implementation. Ready to level up your RL game? Watch the full video now and apply these techniques to your own projects.