Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
The combination of deep reinforcement learning~(RL) and search has achieved human-level performance in board games and various control tasks. Such neuro-symbolic AI has been gaining increasing attention to automate real-world tasks that would be impossible without it. However, the training process usually involves exhaustive exploration of a wide variety of possible scenarios, requiring extensive time and computational resources. To overcome this challenge, we propose an efficient RL algorithm to improve exploration. We introduce trial-and-error exploration that explores states where a critical mistake has happened so that the agent can actively learn to avoid such failures. When the agent recognizes a failure, our method lets the agent retract its action and try different ones until a better action is found. Our evaluation on Cartpole and the board game Othello demonstrated that our method DQN and AlphaZero, that do not have the trial-and-error scheme.
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010