Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This article introduces a model-based reinforcement learning RL approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical on-line optimization of control action sequences. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, Particle Swarm Optimization PSO is applied to search for optimal solutions. This Particle Swarm Optimization Policy PSO-P is effective for high dimensional state spaces and does not require a priori assumptions about adequate policy representations. Furthermore, by translating RL problems into optimization tasks, the rich collection of real-world inspired RL benchmarks is made available for benchmarking numerical optimization techniques. The effectiveness of PSO-P is demonstrated on the two standard benchmarks: mountain car and cart pole.
['Daniel Hein', 'Alexander Hentschel', 'Thomas A. Runkler', 'Steffen Udluft']
I am writing a research paper on a idea Reinforcement Learning with Particle Swarm Optimization Policy PSO-P in Continuous State and Action Spaces
[{"from": "human", "value": "I am writing a research paper on a idea Reinforcement Learning with Particle Swarm Optimization Policy PSO-P in Continuous State and Action Spaces"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This article introduces a model-based reinforcement learning RL approach for continuous state and action spaces. While most RL methods try to find closed-form policies, the approach taken here employs numerical on-line optimization of control action sequences. First, a general method for reformulating RL problems as optimization tasks is provided. Subsequently, Particle Swarm Optimization PSO is applied to search for optimal solutions. This Particle Swarm Optimization Policy PSO-P is effective for high dimensional state spaces and does not require a priori assumptions about adequate policy representations. Furthermore, by translating RL problems into optimization tasks, the rich collection of real-world inspired RL benchmarks is made available for benchmarking numerical optimization techniques. The effectiveness of PSO-P is demonstrated on the two standard benchmarks: mountain car and cart pole."}]