  Model based reinforcement learning has proven to be more sample efficient
than model free methods. On the other hand, the construction of a dynamics
model in model based reinforcement learning has increased complexity. Data
processing tasks in radio astronomy are such situations where the original
problem which is being solved by reinforcement learning itself is the creation
of a model. Fortunately, many methods based on heuristics or signal processing
do exist to perform the same tasks and we can leverage them to propose the best
action to take, or in other words, to provide a `hint'. We propose to use
`hints' generated by the environment as an aid to the reinforcement learning
process mitigating the complexity of model construction. We modify the soft
actor critic algorithm to use hints and use the alternating direction method of
multipliers algorithm with inequality constraints to train the agent. Results
in several environments show that we get the increased sample efficiency by
using hints as compared to model free methods.
