Simulation of Reinforcement Learning Algorithm for Motion Control of an Autonomous Humanoid
Bibliographic record
Abstract
Autonomy is an issue in robotics systems. Currently the robotics research community is focusing on autonomy in the decision. The pre-programmed humanoid robots perform the operation in a known scenario. The presence of an obscure environment, a lack of awareness of the environment, humanoid robot fails to perform the random task. In such cases, the preprogrammed robot needs to be reprogrammed to enable it to perform in the changing environment. There is very limited success achieved in the autonomy of the decision-making process. This research work considered the problem of an autonomy while the deicion making in th real time interaction. The reinforcement algorithm helps to do the task in such type of unstructured and unknown environment. Reinforcement learning problems are categorized into partial Markov Decision Processes (MDP). The goal of RL agent is to minimize its immediate and expected costs. When the system interacts with the Markov Decision Process, RL agent passes through an intermediate sequence of states that depends on another by transition probabilities. The agents action takes, and the agents experience a sequence of immediate costs incurred. Reinforcement Learning and teaching approach like Queue Learning (Q-Learning) is implemented for humanoid robot for navigation and exploration. The Q-learning expresses the expected costs to go of a state action pair defined, which is meant to express the expected costs arising after having taken action in the state following policy. Based on the optimal policy of the reinforcement algorithm, a reinforcement controller was implemented. The transition probabilities of the controller depend on the randomness of the controller. The random values of the controller decide the action. Simulations were carried out for the different positions of the proposed model, and an interesting result were was observed while the transition from the sitting position to the goal position.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".