  In this paper, a novel racing environment for OpenAI Gym is introduced. This
environment operates with continuous action- and state-spaces and requires
agents to learn to control the acceleration and steering of a car while
navigating a randomly generated racetrack. Different versions of two
actor-critic learning algorithms are tested on this environment: Sampled Policy
Gradient (SPG) and Proximal Policy Optimization (PPO). An extension of SPG is
introduced that aims to improve learning performance by weighting action
samples during the policy update step. The effect of using experience replay
(ER) is also investigated. To this end, a modification to PPO is introduced
that allows for training using old action samples by optimizing the actor in
log space. Finally, a new technique for performing ER is tested that aims to
improve learning speed without sacrificing performance by splitting the
training into two parts, whereby networks are first trained using state
transitions from the replay buffer, and then using only recent experiences. The
results indicate that experience replay is not beneficial to PPO in continuous
action spaces. The training of SPG seems to be more stable when actions are
weighted. All versions of SPG outperform PPO when ER is used. The ER trick is
effective at improving training speed on a computationally less intensive
version of SPG.
