  Most reinforcement learning algorithms are inefficient for learning multiple
tasks in complex robotic systems, where different tasks share a set of actions.
In such environments a compound policy may be learnt with shared neural network
parameters, which performs multiple tasks concurrently. However such compound
policy may get biased towards a task or the gradients from different tasks
negate each other, making the learning unstable and sometimes less data
efficient. In this paper, we propose a new approach for simultaneous training
of multiple tasks sharing a set of common actions in continuous action spaces,
which we call as DiGrad (Differential Policy Gradient). The proposed framework
is based on differential policy gradients and can accommodate multi-task
learning in a single actor-critic network. We also propose a simple heuristic
in the differential policy gradient update to further improve the learning. The
proposed architecture was tested on 8 link planar manipulator and 27 degrees of
freedom(DoF) Humanoid for learning multi-goal reachability tasks for 3 and 2
end effectors respectively. We show that our approach supports efficient
multi-task learning in complex robotic systems, outperforming related methods
in continuous action spaces.
