  Designing appropriate reward functions for Reinforcement Learning (RL)
approaches has been a significant problem, especially for complex environments
such as Atari games. Utilizing natural language instructions to provide
intermediate rewards to RL agents in a process known as reward shaping can help
the agent in reaching the goal state faster. In this work, we propose a natural
language-based reward shaping approach that maps trajectories from the
Montezuma's Revenge game environment to corresponding natural language
instructions using an extension of the LanguagE-Action Reward Network (LEARN)
framework. These trajectory-language mappings are further used to generate
intermediate rewards which are integrated into reward functions that can be
utilized to learn an optimal policy for any standard RL algorithms. For a set
of 15 tasks from Atari's Montezuma's Revenge game, the Ext-LEARN approach leads
to the successful completion of tasks more often on average than the reward
shaping approach that uses the LEARN framework and performs even better than
the reward shaping framework without natural language-based rewards.
