  Reinforcement learning agents learn by encouraging behaviours which maximize
their total reward, usually provided by the environment. In many environments,
however, the reward is provided after a series of actions rather than each
single action, leading the agent to experience ambiguity in terms of whether
those actions are effective, an issue known as the credit assignment problem.
In this paper, we propose two strategies inspired by behavioural psychology to
enable the agent to intrinsically estimate more informative reward values for
actions with no reward. The first strategy, called self-punishment (SP),
discourages the agent from making mistakes that lead to undesirable terminal
states. The second strategy, called the rewards backfill (RB), backpropagates
the rewards between two rewarded actions. We prove that, under certain
assumptions and regardless of the reinforcement learning algorithm used, these
two strategies maintain the order of policies in the space of all possible
policies in terms of their total reward, and, by extension, maintain the
optimal policy. Hence, our proposed strategies integrate with any reinforcement
learning algorithm that learns a value or action-value function through
experience. We incorporated these two strategies into three popular deep
reinforcement learning approaches and evaluated the results on thirty Atari
games. After parameter tuning, our results indicate that the proposed
strategies improve the tested methods in over 65 percent of tested games by up
to over 25 times performance improvement.
