OpenAI has released a new batch of seven unsolved problems which have come up in the course of their research at OpenAI. These problems are a fun and meaningful way for new people to enter the field, as well as for practitioners to hone their skills. Many will require inventing new ideas.
- Train an LSTM to solve xor problem.
- Implement a clone of classic Sanke Game and solve it using RL algorithm.
- Slitherin'. : Implement and solve a multiplayer clone of classic Snake game. (Work Going On)
- Parameter Averaging in Distributed RL
- Transfer learning between different games via Generative Models
- Transformers with Linear Attention
- Learned Data Augmentation
- Regularization in Reinforcement Learning
- Automated Solutions of Olympiad Inequality Problems
- Using Double DQN(RL algo) to train the AI agent.
- Using Curriculum Learning approach to train the agent. Basically, keeping the playing area small at the beginning of training. And increasing the size of playing area as the training progresses.
- This helps to solve the problem of Sparse Reward, cause if the agent starts in a very big playing area, it becomes very difficult for it to find positive reward in th environment, thus disabling the agent from learning the optimal solution.
- Small playing area ensures that the agent bumps into the food often, even while performing random actions(epsilon close to 1).
