Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
This addresses the problem of action inefficiency in reinforcement learning for agents, though it is incremental as it builds on existing DQN methods.
The paper tackles the challenge of reinforcement learning with many redundant actions by proposing an Action-Elimination Deep Q-Network (AE-DQN) that eliminates sub-optimal actions, resulting in considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions.
Learning how to act when there are many available actions in each state is a challenging task for Reinforcement Learning (RL) agents, especially when many of the actions are redundant or irrelevant. In such cases, it is sometimes easier to learn which actions not to take. In this work, we propose the Action-Elimination Deep Q-Network (AE-DQN) architecture that combines a Deep RL algorithm with an Action Elimination Network (AEN) that eliminates sub-optimal actions. The AEN is trained to predict invalid actions, supervised by an external elimination signal provided by the environment. Simulations demonstrate a considerable speedup and added robustness over vanilla DQN in text-based games with over a thousand discrete actions.