Large state spaces and self-supervision in reinforcement learning

Reinforcement Learning (RL) is an agent-oriented learning paradigm concerned with learning by interacting with an uncertain environment. Combined with deep neural networks as function approximators, deep reinforcement learning (Deep RL)  allowed recently to tackle highly complex tasks and enable artificial agents to master classic games like Go, play video games from pixels, and solve robotic control tasks.  However, a closer look at these remarkable empirical successes reveals some fundamental limitations. First, it has been challenging to combine desirable features of RL algorithms, such as off-policy and multi-step learning with function approximation in a way that leads to both stable and efficient algorithms in large state spaces. Moreover, Deep RL algorithms tend to be very sample inefficient due to the rudimentary exploration-exploitation strategies these approaches employ. Finally, they require an enormous amount of supervised data and end up producing a narrow agent able to solve only the task that it was trained on. In this thesis, we propose novel solutions to the problems of off-policy learning and exploration-exploitation dilemma in large state spaces, as well as self-supervision in RL.   On the topic of off-policy learning, we provide two contributions. First, for the problem of policy evaluation, we show that combining popular off-policy and multi-step learning methods with linear value function parameterization could lead to undesirable instability, and we derive a provably convergent variant of these methods. Second, for policy optimization, we propose to stabilize the policy improvement step through an off-policy divergence regularization that constrains the discounted state-action visitation induced by consecutive policies to be close to one another.  Next, we study online learning in large state spaces and we focus on two structural assumptions to make the problem tractable: smooth and linear environments. For smooth environments, we propose an efficient online algorithm that actively learns an adaptive partitioning of the joint space by zooming in on more promising and frequently visited regions. For linear environments, we study a more realistic setting, where the environment is now allowed to evolve dynamically and even adversarially over time, but the total change is still bounded. To address this setting, we propose an efficient online algorithm based on weighted least squares value iteration. It uses exponential weights to smoothly forget data that are far in the past, which drives the agent to keep exploring to discover changes.  Finally, beyond the classical RL setting, we consider an agent interacting with its environments without a reward signal. We propose to learn a pair of representations that map state-action pairs to some latent space. During the unsupervised phase, these representations are trained using reward-free interactions to encode long-range relationships between states and actions, via a predictive occupancy map. At test time, once a reward function is revealed, we show that the optimal policy for that reward is directly obtained from these representations, with no planning. This is a step towards building fully controllable agents.  A common theme in the thesis is the design of provable RL algorithms that generalize. In the first and the second part, we deal with generalization in large state spaces either by linear function approximation or state aggregation. In the last part, we focus on generalization over reward functions and we propose a task-agnostic representation learning framework that is provably able to solve all reward functions.

Keyphrases: reinforcement learning, Markov decision process, artificial agent, off-policy learning, function approximation, exploration-exploitation trade-off, self-supervision, generalization, compromis exploration-exploitation