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Title: Combining Q-Learning and Search with Amortized Value Estimates

Paper content:
We introduce "Search with Amortized Value Estimates" (SAVE), an approach for combining model-free Q-learning with model-based Monte-Carlo Tree Search (MCTS).
In SAVE, a learned prior over state-action values is used to guide MCTS, which estimates an improved set of state-action values.
The new Q-estimates are then used in combination with real experience to update the prior.
This effectively amortizes the value computation performed by MCTS, resulting in a cooperative relationship between model-free learning and model-based search.
SAVE can be implemented on top of any Q-learning agent with access to a model, which we demonstrate by incorporating it into agents that perform challenging physical reasoning tasks and Atari.
SAVE consistently achieves higher rewards with fewer training steps, and---in contrast to typical model-based search approaches---yields strong performance with very small search budgets.
By combining real experience with information computed during search, SAVE demonstrates that it is possible to improve on both the performance of model-free learning and the computational cost of planning.
Model-based methods have been at the heart of reinforcement learning (RL) since its inception (Bellman, 1957) , and have recently seen a resurgence in the era of deep learning, with powerful function approximators inspiring a variety of effective new approaches Chua et al., 2018; Wang et al., 2019) .
Despite the success of model-free RL in reaching state-of-the-art performance in challenging domains (e.g. Kapturowski et al., 2018; Haarnoja et al., 2018) , model-based methods hold the promise of allowing agents to more flexibly adapt to new situations and efficiently reason about what will happen to avoid potentially bad outcomes.
The two key components of any such system are the model, which captures the dynamics of the world, and the planning algorithm, which chooses what computations to perform with the model in order to produce a decision or action (Sutton & Barto, 2018) .
Much recent work on model-based RL places an emphasis on model learning rather than planning, typically using generic off-the-shelf planners like Monte-Carlo rollouts or search (see ; Wang et al. (2019) for recent surveys).
Yet, with most generic planners, even a perfect model of the world may require large amounts of computation to be effective in high-dimensional, sparse reward settings.
For example, recent methods which use Monte-Carlo Tree Search (MCTS) require 100s or 1000s of model evaluations per action during training, and even upwards of a million simulations per time step at test time (Anthony et al., 2017; .
These large search budgets are required, in part, because much of the computation performed during planning-such as the estimation of action values-is coarsely summarized in behavioral traces such as visit counts (Anthony et al., 2017; , or discarded entirely after an action is selected (Bapst et al., 2019; Azizzadenesheli et al., 2018) .
However, large search budgets are a luxury that is not always available: many real-world simulators are expensive and may only be feasible to query a handful of times.
In this paper, we explore preserving the value estimates that were computed by search by amortizing them via a neural network and then using this network to guide future search, resulting in an approach which works well even with very small search budgets.
We propose a new method called "Search with Amortized Value Estimates" (SAVE) which uses a combination of real experience as well as the results of past searches to improve overall performance and reduce planning cost.
During training, SAVE uses MCTS to estimate the Q-values at encountered states.
These Q-values are used along with real experience to fit a Q-function, thus amortizing the computation required to estimate values during search.
The Q-function is then used as a prior for subsequent searches, resulting in a symbiotic relationship between model-free learning and MCTS.
At test time, SAVE uses MCTS guided by the learned prior to produce effective behavior, even with very small search budgets and in environments with tens of thousands of possible actions per state-settings which are very challenging for traditional planners.
Here we expand on the results presented in the main text and in Figure 3d and Figure C .2.
Cross-entropy vs. L2 loss While the L2 loss ( Figure C .2, orange) can result in equivalent performance as the cross-entropy loss (Figure C.2, green) , this is at the cost of higher variance across seeds and lower performance on average.
This is likely because the L2 loss encourages the Q-function to exactly match the Q-values estimated by search.
However, with a search budget of 10, those Qvalues will be very noisy.
In contrast, the cross-entropy loss only encourages the Q-function to match the overall distribution shape of the Q-values estimated by search.
This is a less strong constraint that allows the information acquired during search to be exploited while not relying on it too strongly.
Indeed, we can observe that the agent with L2 amortization loss actually performs worse than the agent that has no amortization loss at all ( Figure C .2, purple) when using a search budget of 10, suggesting that trying to match the Q-values during search too closely can harm performance.
Additionally, we can consider an interesting interaction between Q-learning and the amortization loss.
Due to the search locally avoiding poor actions, Q-learning will rarely actually operate on low-valued actions, meaning most of its computation is spent refining the estimates for high-valued actions.
The softmax cross entropy loss ensures that low-valued actions have lower values than high-valued actions, but does not force these values to be exact.
Thus, in this regime we should have good estimates of value for high-valued actions and worse estimates of value for low-valued actions.
In contrast, an L2 loss would require the values to be exact for both low and high valued actions.
By using cross entropy instead, we can allow the neural network to spend more of its capacity representing the high-valued actions and less capacity representing the low-valued actions, which we care less about in the first place anyway.
With vs. without Q-learning Without Q-learning ( Figure C.2, teal) , the SAVE agent's performance suffers dramatically.
As discussed in the previous section, the Q-values estimated during search are very noisy, meaning it is not necessarily a good idea to try to match them exactly.
Additionally, Q MCTS is on-policy experience and can become stale if Q θ changes too much between when Q MCTS was computed and when it is used for learning.
Thus, removing the Q-learning loss makes the learning algorithm much more on-policy and therefore susceptible to the issues that come with on-policy training.
Indeed, without the Q-learning loss, we can only rely on the Q-values estimated during search, resulting in much worse performance than when Q-learning is used.
UCT vs. PUCT Finally, we compared to a variant which utilizes prior knowledge by transforming the Q-values into a policy via a softmax and then using this policy as a prior with PUCT, rather than using it to initialize the Q-values (Figure C.2, brown) .
With large amounts of search, the initial setting of the Q-values should not matter much, but in the case of small search budgets (as seen here), the estimated Q-values do not change much from their initial values.
Thus, if the initial values are zero, then the final values will also be close to zero, which later results in the Q-function being regressed towards a nearly uniform distribution of value.
By initializing the Q-values with the Qfunction, the values that are regressed towards may be similar to the original Q-function but will not be uniform.
Thus, we can more effectively reuse knowledge across multiple searches by initializing the Q-values with UCT rather than incorporating prior knowledge via PUCT.

assistant: text: We propose a model-based method called "Search with Amortized Value Estimates" (SAVE) which leverages both real and planned experience by combining Q-learning with Monte-Carlo Tree Search, achieving strong performance with very small search budgets.