the network performance, particularly in densely deployed networks, since the potential parents may also perform poorly. One reason is that in RPL networks, the predominant choice is to set the transmission power to the maximum for all the nodes to simplify the operation of the protocol. Alternatively, the wireless nodes can adjust their transmission power to increase spatial reuse in the network consequently improving the quality of service under dense and heavy traffic scenarios. First, it may increase channel contention and reduce spatial reuse in the channel. Consequently, nodes waste their limited energy due to (i) unnecessary large values of transmission power and (ii) the re-transmission of packets due to collision with other nodes. On the other hand, radically reducing the transmission power may compromise the network connectivity since receivers may experience a lower Signal-to-Noise Ratio (SNR) [7]. Commercially available off-the-shelf (COTS) radios provide metrics such as the _Receiver Signal Strength Indicator_ (RSSI) and _Link Quality Indicator_ (LQI). These metrics provide an instantaneous metric to decide on the transmission power but are volatile in nature and not very accurate for the long-term estimation of wireless link quality [8; 9]. That is why accurate methods require nodes to probe different transmission power settings continuously, which implies severe overhead to the network. Moreover, adding extra control traffic escalates congestion, which is the underlying cause of packet loss and delay. By passively measuring the delivery rate of various settings, node can infer the link quality without requiring additional control traffic. Passive probing can be costly if it is not performed properly, as nodes may sacrifice some data packets by reducing the transmission power for the sake of exploring the opportunity. Some existing works probe different transmission powers using simple algorithms without providing a theoretical background on convergence to the optimal setting. For instance, window-based protocols [10] assume they have found the best transmission power if they successfully transmit a certain number of packets. Our preliminary study shows that, even in a simple two-node scenario, this assumption is unrealistic. In such settings, these algorithms might not select the optimal transmission power or converge very slowly. An intelligent exploration strategy is required to ensure the probing of different settings is performed faster and is guaranteed to converge. We resorted to the exploration-exploitation dilemma in the _Multi-Armed Bandits_ framework and UCB algorithm to optimize the probing of the transmission power settings. _Multi-Armed Bandits_ are a class of reinforcement algorithms in which a single-state agent has no prior knowledge of the rewards. In this framework, each node simultaneously learns and optimizes its rewards. We present _Adaptive Control of Transmission Power for RPL_ (ACTOR), which includes a set of methods that extend RPL to adapt transmission power settings based on bandit theory. ACTOR aims to increase the throughput while minimizing the transmission power in dense and high-traffic LLNs. This also has the potential to preserve energy in the batteries through spatial reuse and lower internal interference. ACTOR takes advantage of the routing information in the transmission power control. The reliable communication of nodes in a multi-hop network hinges on the parents to relay the traffic. When a parent node reduces its transmission power, it is not supposed to jeopardize communications with its children. In ACTOR, children inform their preferred parent of their expected transmission power settings so the parents do not abandon their children. To further accelerate the convergence of the learning process, ACTOR also employs a blacklisting mechanism to filter out transmission power settings that are too unreliable. Importantly, ACTOR is backward-compatible with the RPL protocol and easy to implement in COTS devices with minimal overhead.