README.md

Task Planning with Pretrained Prior

Consider the problem of determining the next action of an agent trying to accomplish a goal, $g$, the current and previous observations $o_{1:t}$, as well as the previous actions $a_{1:t-1}$.

$$p(a_t|g, o_{1:t}, a_{1:t-1})$$

We can factor out a prior by formulating using Bayes Rule:

$$p(g, o_{1:t-1}|a_{1:t-1}, a_t, o_t)p(a_t|a_{1:t-1}, o_t)$$

where the first term is the likelihood and second prior, which can be a pretrained language model if $o_t$ is encoded using text. Observe the likelihood is discrimative i.e. it operates on binary labels/outputs probability that denote whether $a_t$ is the correct action. Assume that all goals come can unified relatively well under the same prior, and the training data for prior fine-tuning can be aggregated from multiple goals. We can sample actions as follows: sample the top $k$ choices from the prior and reweight them using the likelihood.

In our case, we use BabyAI as the environment. The prior is GPT2 fine-tuned on many goals, where $a_{1:t-1}, o_t$ are represented as text. The likelihood is a classifier that involves BabyAI's 7x7 grid, the aggregated actions, observations, and the goal. We generate negative samples by changing the action of the last element of a goal sequence, which can be viewed as analogous to teacher forcing.

The prior achieves decent accuracy (~70%) and the likelihoods achieve very high accuracy (~93%) with low data (few hundred data points) only on the given goal.

View detailed experiment logs here.