2,266 research outputs found
Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic
We consider the problem of using a heuristic policy to improve the value
approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in
non-adversarial settings such as planning with large-state space Markov
Decision Processes. Current improvements to UCT focus on either changing the
action selection formula at the internal nodes or the rollout policy at the
leaf nodes of the search tree. In this work, we propose to add an auxiliary arm
to each of the internal nodes, and always use the heuristic policy to roll out
simulations at the auxiliary arms. The method aims to get fast convergence to
optimal values at states where the heuristic policy is optimal, while retaining
similar approximation as the original UCT in other states. We show that
bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs
better compared to the original UCT algorithm and its variants in two benchmark
experiment settings. We also examine conditions under which UCT-Aux works well.Comment: 16 pages, accepted for presentation at ECML'1
Credit assignment in multiple goal embodied visuomotor behavior
The intrinsic complexity of the brain can lead one to set aside issues related to its relationships with the body, but the field of embodied cognition emphasizes that understanding brain function at the system level requires one to address the role of the brain-body interface. It has only recently been appreciated that this interface performs huge amounts of computation that does not have to be repeated by the brain, and thus affords the brain great simplifications in its representations. In effect the brain’s abstract states can refer to coded representations of the world created by the body. But even if the brain can communicate with the world through abstractions, the severe speed limitations in its neural circuitry mean that vast amounts of indexing must be performed during development so that appropriate behavioral responses can be rapidly accessed. One way this could happen would be if the brain used a decomposition whereby behavioral primitives could be quickly accessed and combined. This realization motivates our study of independent sensorimotor task solvers, which we call modules, in directing behavior. The issue we focus on herein is how an embodied agent can learn to calibrate such individual visuomotor modules while pursuing multiple goals. The biologically plausible standard for module programming is that of reinforcement given during exploration of the environment. However this formulation contains a substantial issue when sensorimotor modules are used in combination: The credit for their overall performance must be divided amongst them. We show that this problem can be solved and that diverse task combinations are beneficial in learning and not a complication, as usually assumed. Our simulations show that fast algorithms are available that allot credit correctly and are insensitive to measurement noise
Pseudorehearsal in value function approximation
Catastrophic forgetting is of special importance in reinforcement learning,
as the data distribution is generally non-stationary over time. We study and
compare several pseudorehearsal approaches for Q-learning with function
approximation in a pole balancing task. We have found that pseudorehearsal
seems to assist learning even in such very simple problems, given proper
initialization of the rehearsal parameters
Thinking Fast and Slow with Deep Learning and Tree Search
Sequential decision making problems, such as structured prediction, robotic
control, and game playing, require a combination of planning policies and
generalisation of those plans. In this paper, we present Expert Iteration
(ExIt), a novel reinforcement learning algorithm which decomposes the problem
into separate planning and generalisation tasks. Planning new policies is
performed by tree search, while a deep neural network generalises those plans.
Subsequently, tree search is improved by using the neural network policy to
guide search, increasing the strength of new plans. In contrast, standard deep
Reinforcement Learning algorithms rely on a neural network not only to
generalise plans, but to discover them too. We show that ExIt outperforms
REINFORCE for training a neural network to play the board game Hex, and our
final tree search agent, trained tabula rasa, defeats MoHex 1.0, the most
recent Olympiad Champion player to be publicly released.Comment: v1 to v2: - Add a value function in MCTS - Some MCTS hyper-parameters
changed - Repetition of experiments: improved accuracy and errors shown.
(note the reduction in effect size for the tpt/cat experiment) - Results from
a longer training run, including changes in expert strength in training -
Comparison to MoHex. v3: clarify independence of ExIt and AG0. v4: see
appendix
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