Is Curiosity All You Want? On the Utility of Emergent Behaviours from Curious Exploration

Throughout purely curious exploration, the JACO arm discovers the right way to choose up cubes, strikes them across the workspace and even explores whether or not they are often balanced on their edges.

Curious exploration allows OP3 to stroll upright, steadiness on one foot, sit down and even catch itself safely when leaping backwards – all and not using a particular goal job to optimise for.

Intrinsic motivation [1, 2] could be a highly effective idea to endow an agent with a mechanism to repeatedly discover its atmosphere within the absence of job info. One frequent technique to implement intrinsic motivation is by way of curiosity studying [3, 4]. With this methodology, a predictive mannequin concerning the atmosphere’s response to an agent’s actions is skilled alongside the agent’s coverage. This mannequin will also be referred to as a world mannequin. When an motion is taken, the world mannequin makes a prediction concerning the agent’s subsequent statement. This prediction is then in comparison with the true statement made by the agent. Crucially, the reward given to the agent for taking this motion is scaled by the error it made when predicting the subsequent statement. This fashion, the agent is rewarded for taking actions whose outcomes are usually not but nicely predictable. Concurrently, the world mannequin is up to date to raised predict the end result of stated motion.

This mechanism has been utilized efficiently in on-policy settings, e.g. to beat 2D pc video games in an unsupervised manner [4] or to coach a basic coverage which is well adaptable to concrete downstream duties [5]. Nevertheless, we consider that the true power of curiosity studying lies within the various behaviour which emerges throughout the curious exploration course of: Because the curiosity goal adjustments, so does the ensuing behaviour of the agent thereby discovering many complicated insurance policies which may very well be utilised in a while, in the event that they had been retained and never overwritten.

In this paper, we make two contributions to review curiosity studying and harness its emergent behaviour: First, we introduce SelMo, an off-policy realisation of a self-motivated, curiosity-based methodology for exploration. We present that utilizing SelMo, significant and various behaviour emerges solely based mostly on the optimisation of the curiosity goal in simulated manipulation and locomotion domains. Second, we suggest to increase the main target within the software of curiosity studying in direction of the identification and retention of rising intermediate behaviours. We assist this conjecture with an experiment which reloads self-discovered behaviours as pretrained, auxiliary expertise in a hierarchical reinforcement studying setup.

The management circulate of the SelMo methodology: The agent (actor) collects trajectories within the atmosphere utilizing its present coverage and shops them within the mannequin replay buffer on the left. The related world mannequin samples uniformly that buffer and updates its parameters for ahead prediction utilizing stochastic gradient descent (SGD). The sampled trajectories are assigned curiosity rewards scaled by their respective prediction error underneath the present world mannequin. The labeled trajectories are then handed on to the coverage replay buffer on the best. Most a posteriori coverage optimisation (MPO) [6] is used to suit Q-function and coverage based mostly on samples from the coverage replay. The ensuing, up to date coverage is then synced again into the actor.

We run SelMo in two simulated steady management robotic domains: On a 6-DoF JACO arm with a three-fingered gripper and on a 20-DoF humanoid robotic, the OP3. The respective platforms current difficult studying environments for object manipulation and locomotion, respectively. Whereas solely optimising for curiosity, we observe that complicated human-interpretable behaviour emerges over the course of the coaching runs. As an illustration, JACO learns to select up and transfer cubes with none supervision or the OP3 learns to steadiness on a single foot or sit down safely with out falling over.

Instance coaching timelines for JACO and the OP3. Whereas optimising for the curiosity goal, complicated, significant behaviour emerges in each manipulation and locomotion settings. The complete movies might be discovered on the prime of this web page.

Nevertheless, the spectacular behaviours noticed throughout curious exploration have one essential downside: They aren’t persistent as they maintain altering with the curiosity reward perform. Because the agent retains repeating a sure behaviour, e.g. JACO lifting the pink dice, the curiosity rewards amassed by this coverage are diminishing. Consequently, this results in the training of a modified coverage which acquires larger curiosity rewards once more, e.g. transferring the dice outdoors the workspace and even attending to the opposite dice. However this new behaviour overwrites the outdated one. Nevertheless, we consider that retaining the emergent behaviours from curious exploration equips the agent with a helpful talent set to be taught new duties extra shortly. With a purpose to examine this conjecture, we arrange an experiment to probe the utility of the self-discovered expertise.

We deal with randomly sampled snapshots from totally different phases of the curious exploration as auxiliary expertise in a modular studying framework [7] and measure how shortly a brand new goal talent might be realized by utilizing these auxiliaries. Within the case of the JACO arm, we set the goal job to be “raise the pink dice” and use 5 randomly sampled self-discovered behaviours as auxiliaries. We evaluate the training of this downstream job to an SAC-X baseline [8] which makes use of a curriculum of reward capabilities to reward reaching and transferring the pink dice which finally facilitates to be taught lifting as nicely. We discover that even this straightforward setup for skill-reuse already accelerates the training progress of the downstream job commensurate with a hand designed reward curriculum. The outcomes recommend that the automated identification and retention of helpful rising behaviour from curious exploration is a fruitful avenue of future investigation in unsupervised reinforcement studying.

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