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I want to create a chatbot for a translation engine called Bashini, an AI-led la
I want to create a chatbot for a translation engine called Bashini, an AI-led language translation system that enables people to speak in their own language while talking to speakers of other Indian languages using Reinforcement learning with poor reward signals.
Instructions from professor:
The reward signal, which defines the goal of a Reinforcement Learning (RL) agent, is a critical part of any RL problem. For many real-world RL problems, however, the rewards are often quite sparse, most of the time only indicating whether the task is completed partially or fully. Such sparse rewards provide the agent with only rare signs of progress, making the learning of the task slow and difficult. In some scenarios, the rewards might be noisy, change over time, or even be completely unavailable for extended periods of time. Some real-world RL problems also involve goals that are difficult to translate precisely into a numerical reward signal. Learning with such poor reward signals poses serious challenges to current RL methods.
I am interested in developing methods that would enable RL agents to learn effectively in environments with poor reward signals. Different types of poor reward signal scenarios pose different types of challenges for agent learning, requiring different types of solutions. Some promising solution directions that we could pursue include (but are not limited to) 1) providing additional learning signals to the agent (e.g., intrinsic rewards and auxiliary tasks), 2) transferring knowledge from other AI agents or external knowledge sources (e.g., from textbooks, manuals, and the web), 3) decomposing the problem into simpler subproblems (hierarchical RL), and 4) building reward models (model-based RL) utilizing other forms of communicating the task beyond numerical rewards (e.g., from demonstrations and human preferences/feedback).
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