Overcome Sparse Rewards in Reinforcement Learning

Reinforcement learning (RL) in sparse reward environments poses a unique challenge. The scarcity of positive feedback can lead agents to plateau early or get stuck in local optima. In this article, we explore how to overcome these hurdles using the ACER (Actor-Critic with Experience Replay) algorithm. We’ll delve into the intricacies of tuning hyperparameters, reward shaping, and employing prioritized experience replay (PER) to improve the performance of your RL agents in sparse reward scenarios.

Why ACER? ACER stands out in environments with sparse rewards due to its robust use of experience replay and a trust region constraint that stabilizes learning. Unlike other policy gradient methods, ACER can manage off-policy data while maintaining reliable updates, making it an ideal choice for complex environments like Squid Hunt, where rewards are infrequent.

Overcoming Sparse Rewards: In sparse reward systems, agents often struggle with exploration and fail to discover optimal strategies. To combat this, we implemented several key techniques:

1. Hyperparameter Tuning: Adjusting learning rates for both actor and critic networks was crucial. Lower learning rates were used to allow the agent to learn more gradually, preventing premature convergence. The gamma (discount factor) was also reduced to focus more on immediate rewards, which are rare but significant in this environment.
2. Prioritized Experience Replay (PER): PER was essential in our approach. By prioritizing transitions with higher rewards or significant negative outcomes (like hitting a wall in Squid Hunt), the agent was able to learn more effectively from its experiences. This helped in focusing on the most informative transitions, leading to more stable policy updates.
3. Reward Shaping: We rescaled rewards to be between -1 and 1, which helped in stabilizing the learning process. This normalization allowed the agent to better understand the significance of different actions without being overwhelmed by large reward signals that could lead to instability.
4. Preventing Policy Collapse: One of the biggest challenges was preventing the agent from becoming too certain of suboptimal actions early on. By increasing the entropy coefficient, we encouraged more exploration, allowing the agent to discover higher-reward strategies that it might have otherwise ignored.

Results: With these strategies, the reward increased to an average of 5, a significant improvement from the initial value. However, the learning process still encountered plateaus, indicating that further fine-tuning might be necessary. This could involve experimenting with even higher entropy coefficients or adjusting the PER strategy to further emphasize rare high-reward transitions.

Conclusion: Designing RL algorithms for sparse reward environments requires a delicate balance of exploration and exploitation. ACER, with its unique features, provides a solid foundation, but success hinges on carefully tuned hyperparameters, effective reward shaping, and strategic use of PER. By applying these techniques, you can enhance your RL agent’s ability to thrive in even the most challenging environments.

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