MDP Playground: Controlling Orthogonal Dimensions of Hardness in Toy Environments

We present MDP Playground, an efficient benchmark for Reinforcement Learning (RL) algorithms with various dimensions of hardness that can be controlled independently to challenge algorithms in different ways and to obtain varying degrees of hardness in generated environments. We consider and allow control over a wide variety of key hardness dimensions, including delayed rewards, rewardable sequences, sparsity of rewards, stochasticity, image representations, irrelevant features, time unit, and action max. While it is very time consuming to run RL algorithms on standard benchmarks, we define a parameterised collection of fast-to-run toy benchmarks in OpenAI Gym by varying these dimensions. Despite their toy nature and low compute requirements, we show that these benchmarks present substantial challenges to current RL algorithms. Furthermore, since we can generate environments with a desired value for each of the dimensions, in addition to having fine-grained control over the environments' hardness, we also have the ground truth available for evaluating algorithms. Finally, we evaluate the kinds of transfer for these dimensions that may be expected from our benchmarks to more complex benchmarks. We believe that MDP Playground is a valuable testbed for researchers designing new, adaptive and intelligent RL algorithms and those wanting to unit test their algorithms.