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Found 5 papers, 0 papers with code
Lifelike Agility and Play on Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
In this work, we propose a framework for driving legged robots act like real animals with lifelike agility and strategy in complex environments.
Relative Policy-Transition Optimization for Fast Policy Transfer
Integrating the two algorithms results in the complete Relative Policy-Transition Optimization (RPTO) algorithm, in which the policy interacts with the two environments simultaneously, such that data collections from two environments, policy and transition updates are completed in one closed loop to form a principled learning framework for policy transfer.
A General Theory of Relativity in Reinforcement Learning
We propose a new general theory measuring the relativity between two arbitrary Markov Decision Processes (MDPs) from the perspective of reinforcement learning (RL).
Trade-off on Sim2Real Learning: Real-world Learning Faster than Simulations
While considering Sim and Real learning, our experiments show that the sample-efficient Deep Bayesian RL performance is better than DRL even when computation time (as opposed to number of iterations) is taken in consideration.
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals
Deep Reinforcement Learning (DRL) has shown its promising capabilities to learn optimal policies directly from trial and error.
