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Title: From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following

Paper content:
Reinforcement learning is a promising framework for solving control problems, but its use in practical situations is hampered by the fact that reward functions are often difficult to engineer.
Specifying goals and tasks for autonomous machines, such as robots, is a significant challenge: conventionally, reward functions and goal states have been used to communicate objectives.
But people can communicate objectives to each other simply by describing or demonstrating them.
How can we build learning algorithms that will allow us to tell machines what we want them to do?
In this work, we investigate the problem of grounding language commands as reward functions using inverse reinforcement learning, and argue that language-conditioned rewards are more transferable than language-conditioned policies to new environments.
We propose language-conditioned reward learning (LC-RL), which grounds language commands as a reward function represented by a deep neural network.
We demonstrate that our model learns rewards that transfer to novel tasks and environments on realistic, high-dimensional visual environments with natural language commands, whereas directly learning a language-conditioned policy leads to poor performance.
Figure 1 : A task where an agent (green triangle) must execute the command "go to the fruit bowl."
This is a simple example where the reward function is easier to specify than the policy.While reinforcement learning provides a powerful and flexible framework for describing and solving control tasks, it requires the practitioner to specify objectives in terms of reward functions.
Engineering reward functions is often done by experienced practitioners and researchers, and even then can pose a significant challenge, such as when working with complex image-based observations.
While researchers have investigated alternative means of specifying objectives, such as learning from demonstration BID1 , or through binary preferences BID5 , language is often a more natural and desirable way for humans to communicate goals.A common approach to building natural language interfaces for reinforcement learning agents is to build language-conditioned policies that directly map observations and language commands to a sequence of actions that perform the desired task.
However, this requires the policy to solve two challenging problems together: understanding how to plan and solve tasks in the physical world, and understanding the language command itself.
The trained policy must simultaneously interpret a command and plan through possibly complicated environment dynamics.
The performance of the system then hinges entirely on its ability to generalize to new environments -if either the language interpretation or the physical control fail to generalize, the entire system will fail.
We can recognize instead that the role of language in such a system is to communicate the goal, and rather than mapping language directly to policies, we propose to learn how to convert language-defined goals into reward functions.
In this manner, the agent can learn how to plan and perform the task on its own via reinforcement learning, directly interacting with the environment, without relying on zero-shot transfer of policies.
A simple example is shown in Figure 1 , where an agent is tasked with navigating through a house.
If an agent is commanded "go to the fruit bowl", a valid reward function could simply be a fruit bowl detector from first-person views of the agent.
However, if we were to learn a mapping from language to actions, given the same goal description, the model would need to generate a different plan for each house.In this work, we investigate the feasibility of grounding free-form natural language commands as reward functions using inverse reinforcement learning (IRL).
Learning language-conditioned rewards poses unique computational problems.
IRL methods generally require solving a reinforcement learning problem as an inner-loop BID26 , or rely on potentially unstable adversarial optimization procedures BID8 BID10 .
This is compounded by the fact that we wish to train our model across multiple tasks, meaning the IRL problem itself is an inner-loop.
In order to isolate the language-learning problem from the difficulties in solving reinforcement learning and adversarial learning problems, we base our method on an exact MaxEnt IRL BID26 procedure, which requires full knowledge of environment dynamics to train a language-conditioned reward function represented by a deep neural network.
While using exact IRL procedures may seem limiting, in many cases (such as indoor robotic navigation) full environment dynamics are available, and this formulation allows us to remove the difficulty of using RL from the training procedure.
The crucial insight is that we can use dynamic programming methods during training to learn a reward function that maps from observations, but we do not need knowledge of dynamics to use the reward function, meaning during test time we can evaluate using a reinforcement learning agent without knowledge of the underlying environment dynamics.
We evaluate our method on a dataset of realistic indoor house navigation and pick-and-place tasks using the SUNCG dataset, with natural language commands.
We demonstrate that our approach generalizes not only to novel tasks, but also to entirely new scenes, while directly learning a language-conditioned policy leads to poor performance and fails to generalize.
In this paper, we introduced LC-RL, an algorithm for scalable training of language-conditioned reward functions represented by neural networks.
Our method restricts training to tractable domains with known dynamics, but learns a reward function which can be used with standard RL methods in environments with unknown dynamics.
We demonstrate that the reward-learning approach to instruction following outperforms the policy-learning when evaluated in test environments, because the reward-learning enables an agent to learn and interact within the test environment rather than relying on zero-shot policy transfer.

assistant: text: We ground language commands in a high-dimensional visual environment by learning language-conditioned rewards using inverse reinforcement learning.