Actor Prioritized Experience Replay
This addresses a stability and performance problem in deep reinforcement learning for continuous control domains, offering a novel improvement to PER.
The paper tackled the underperformance of Prioritized Experience Replay (PER) in continuous control actor-critic algorithms by theoretically showing that actor networks cannot be effectively trained with transitions having large TD errors, leading to divergence in policy gradients. The introduced novel experience replay sampling framework significantly outperforms competing approaches and achieves state-of-the-art results in standard off-policy actor-critic algorithms.
A widely-studied deep reinforcement learning (RL) technique known as Prioritized Experience Replay (PER) allows agents to learn from transitions sampled with non-uniform probability proportional to their temporal-difference (TD) error. Although it has been shown that PER is one of the most crucial components for the overall performance of deep RL methods in discrete action domains, many empirical studies indicate that it considerably underperforms actor-critic algorithms in continuous control. We theoretically show that actor networks cannot be effectively trained with transitions that have large TD errors. As a result, the approximate policy gradient computed under the Q-network diverges from the actual gradient computed under the optimal Q-function. Motivated by this, we introduce a novel experience replay sampling framework for actor-critic methods, which also regards issues with stability and recent findings behind the poor empirical performance of PER. The introduced algorithm suggests a new branch of improvements to PER and schedules effective and efficient training for both actor and critic networks. An extensive set of experiments verifies our theoretical claims and demonstrates that the introduced method significantly outperforms the competing approaches and obtains state-of-the-art results over the standard off-policy actor-critic algorithms.