Three Ways to Fail at Manufacturing a First Success

The text explores various strategies for achieving successful task completion in reinforcement learning experiments, focusing on the limitations of using partial credit and memory systems in unsolved tasks. The experiments involve running an agent through tasks and using a testing suite to score performance, which highlights the difficulty of achieving a full success when relying solely on past failures or partial credit. Different approaches, such as guided exploration and high-temperature sampling, were tested but failed to consistently produce successful outcomes, emphasizing the need for a direct demonstration or imitation framework to improve agent performance. The document underscores the challenges of optimizing policies based on partial feedback and the necessity of starting with a successful example to guide learning. It also discusses how context bootstrapped reinforcement learning and supervised training on demonstrations can lead to better results. The experiments reveal that the model's architecture and task complexity significantly impact the ability to achieve complete task success, suggesting that providing a clear demonstration is a more effective strategy compared to relying on autonomous exploration in complex environments.

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