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Title: Optimistic Exploration even with a Pessimistic Initialisation

Paper content:
Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL).
In the tabular case, all provably efficient model-free algorithms rely on it.
However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient tabular algorithms.
In particular, in scenarios with only positive rewards, Q-values are initialised at their lowest possible values due to commonly used network initialisation schemes, a pessimistic initialisation.
Merely initialising the network to output optimistic Q-values is not enough, since we cannot ensure that they remain optimistic for novel state-action pairs, which is crucial for exploration.
We propose a simple count-based augmentation to pessimistically initialised Q-values that separates the source of optimism from the neural network.
We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting.
Our algorithm, Optimistic Pessimistically Initialised Q-Learning (OPIQ), augments the Q-value estimates of a DQN-based agent with count-derived bonuses to ensure optimism during both action selection and bootstrapping.
We show that OPIQ outperforms non-optimistic DQN variants that utilise a pseudocount-based intrinsic motivation in hard exploration tasks, and that it predicts optimistic estimates for novel state-action pairs.
In reinforcement learning (RL), exploration is crucial for gathering sufficient data to infer a good control policy.
As environment complexity grows, exploration becomes more challenging and simple randomisation strategies become inefficient.
While most provably efficient methods for tabular RL are model-based (Brafman and Tennenholtz, 2002; Strehl and Littman, 2008; Azar et al., 2017) , in deep RL, learning models that are useful for planning is notoriously difficult and often more complex (Hafner et al., 2019) than modelfree methods.
Consequently, model-free approaches have shown the best final performance on large complex tasks (Mnih et al., 2015; 2016; Hessel et al., 2018) , especially those requiring hard exploration (Bellemare et al., 2016; Ostrovski et al., 2017) .
Therefore, in this paper, we focus on how to devise model-free RL algorithms for efficient exploration that scale to large complex state spaces and have strong theoretical underpinnings.
Despite taking inspiration from tabular algorithms, current model-free approaches to exploration in deep RL do not employ optimistic initialisation, which is crucial to provably efficient exploration in all model-free tabular algorithms.
This is because deep RL algorithms do not pay special attention to the initialisation of the neural networks and instead use common initialisation schemes that yield initial Q-values around zero.
In the common case of non-negative rewards, this means Q-values are initialised to their lowest possible values, i.e., a pessimistic initialisation.
While initialising a neural network optimistically would be trivial, e.g., by setting the bias of the final layer of the network, the uncontrolled generalisation in neural networks changes this initialisation quickly.
Instead, to benefit exploration, we require the Q-values for novel state-action pairs must remain high until they are explored.
An empirically successful approach to exploration in deep RL, especially when reward is sparse, is intrinsic motivation (Oudeyer and Kaplan, 2009) .
A popular variant is based on pseudocounts (Bellemare et al., 2016) , which derive an intrinsic bonus from approximate visitation counts over states and is inspired by the tabular MBIE-EB algorithm (Strehl and Littman, 2008) .
However, adding a positive intrinsic bonus to the reward yields optimistic Q-values only for state-action pairs that have already been chosen sufficiently often.
Incentives to explore unvisited states rely therefore on the generalisation of the neural network.
Exactly how the network generalises to those novel state-action pairs is unknown, and thus it is unclear whether those estimates are optimistic when compared to nearby visited state-action pairs.
Figure 1 Consider the simple example with a single state and two actions shown in Figure  1 .
The left action yields +0.1 reward and the right action yields +1 reward.
An agent whose Q-value estimates have been zero-initialised must at the first time step select an action randomly.
As both actions are underestimated, this will increase the estimate of the chosen action.
Greedy agents always pick the action with the largest Q-value estimate and will select the same action forever, failing to explore the alternative.
Whether the agent learns the optimal policy or not is thus decided purely at random based on the initial Q-value estimates.
This effect will only be amplified by intrinsic reward.
To ensure optimism in unvisited, novel state-action pairs, we introduce Optimistic Pessimistically Initialised Q-Learning (OPIQ).
OPIQ does not rely on an optimistic initialisation to ensure efficient exploration, but instead augments the Q-value estimates with count-based bonuses in the following manner:
where N (s, a) is the number of times a state-action pair has been visited and M, C > 0 are hyperparameters.
These Q + -values are then used for both action selection and during bootstrapping, unlike the above methods which only utilise Q-values during these steps.
This allows OPIQ to maintain optimism when selecting actions and bootstrapping, since the Q + -values can be optimistic even when the Q-values are not.
In the tabular domain, we base OPIQ on UCB-H (Jin et al., 2018) , a simple online Q-learning algorithm that uses count-based intrinsic rewards and optimistic initialisation.
Instead of optimistically initialising the Q-values, we pessimistically initialise them and use Q + -values during action selection and bootstrapping.
Pessimistic initialisation is used to enable a worst case analysis where all of our Q-value estimates underestimate Q * and is not a requirement for OPIQ.
We show that these modifications retain the theoretical guarantees of UCB-H.
Furthermore, our algorithm easily extends to the Deep RL setting.
The primary difficulty lies in obtaining appropriate state-action counts in high-dimensional and/or continuous state spaces, which has been tackled by a variety of approaches (Bellemare et al., 2016; Ostrovski et al., 2017; Tang et al., 2017; Machado et al., 2018a) and is orthogonal to our contributions.
We demonstrate clear performance improvements in sparse reward tasks over
1) a baseline DQN that just uses intrinsic motivation derived from the approximate counts,
2) simpler schemes that aim for an optimistic initialisation when using neural networks, and
3) strong exploration baselines.
We show the importance of optimism during action selection for ensuring efficient exploration.
Visualising the predicted Q + -values shows that they are indeed optimistic for novel state-action pairs.
This paper presented OPIQ, a model-free algorithm that does not rely on an optimistic initialisation to ensure efficient exploration.
Instead, OPIQ augments the Q-values estimates with a count-based optimism bonus.
We showed that this is provably efficient in the tabular setting by modifying UCB-H to use a pessimistic initialisation and our augmented Q + -values for action selection and bootstrapping.
Since our method does not rely on a specific initialisation scheme, it easily scales to deep RL when paired with an appropriate counting scheme.
Our results showed the benefits of maintaining optimism both during action selection and bootstrapping for exploration on a number of hard sparse reward environments including Montezuma's Revenge.
In future work, we aim to extend OPIQ by integrating it with more expressive counting schemes.

assistant: text: We augment the Q-value estimates with a count-based bonus that ensures optimism during action selection and bootstrapping, even if the Q-value estimates are pessimistic.