Human feedback in continuous actor-critic reinforcement learning

Reinforcement learning is utilized in contexts where an agent tries to learn from the environment. Using continuous actions, the performance may be improved in comparison to using discrete actions, however, this leads to excessive time to find a proper policy. In this work, we focus on including human feedback in reinforcement learning for a continuous action space. We unify the policy and the feedback to favor actions of low probability density. Furthermore, we compare the performance of the feedback for the continuous actor-critic algorithm and test our experiments in the cart-pole balancing task. The obtained results show that the proposed approach increases the accumulated reward in comparison to the autonomous learning method.