4 accepted papers
Empathy allows us to assume others are like us and have goals analogous to our own. This can also at times be applied to multi-agent games - e.g. Agent 1's attraction to green balls is analogous to Agent 2's attraction to red balls. Drawing inspiration from empathy, we propose EMOTE, a simple and…
We introduce a constrained optimization method for policy gradient reinforcement learning, which uses two trust regions to regulate each policy update. In addition to using the proximity of one single old policy as the first trust region as done by prior works, we propose forming a second trust regi…
Transfer in reinforcement learning is usually achieved through generalisation across tasks. Whilst many studies have investigated transferring knowledge when the reward function changes, they have assumed that the dynamics of the environments remain consistent. Many real-world RL problems require tr…
Episodic control enables sample efficiency in reinforcement learning by recalling past experiences from an episodic memory. We propose a new model-based episodic memory of trajectories addressing current limitations of episodic control. Our memory estimates trajectory values, guiding the agent towar…